[
  {
    "path": ".github/FUNDING.yml",
    "content": "# These are supported funding model platforms\n\ngithub: HALLRESEARCH-AI\n"
  },
  {
    "path": "LICENSE",
    "content": "Creative Commons Legal Code\n\nCC0 1.0 Universal\n\n    CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE\n    LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN\n    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS\n    INFORMATION ON AN \"AS-IS\" BASIS. 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  },
  {
    "path": "README.md",
    "content": "# Awesome Machine Learning Interpretability [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA maintained and curated list of practical and awesome responsible machine learning resources.\n\nIf you want to contribute to this list (*and please do!*), read over the [contribution guidelines](contributing.md), send a [pull request](https://github.com/jphall663/awesome-machine-learning-interpretability/compare), or file an [issue](https://github.com/jphall663/awesome-machine-learning-interpretability/issues/new).\n\nIf something you contributed or found here is missing, please check the [archive](https://github.com/jphall663/awesome-machine-learning-interpretability/blob/master/archive).\n\n![](HR-logo-350x100.png)\n\nMaintenance and curation sponsored by [HallResearch.ai](https://www.hallresearch.ai).\n\n## Contents\n\n* **Community and Official Guidance Resources**\n  * [Community Frameworks and Guidance](#community-frameworks-and-guidance)\n    * [Infographics and Cheat Sheets](#infographics-and-cheat-sheets)\n    * [AI Red-Teaming Resources](#ai-red-teaming-resources)\n    * [Generative AI Explainability](#generative-ai-explainability)\n    * [University Policies and Guidance](#university-policies-and-guidance)\n  * [Official Policy, Frameworks, and Guidance](#official-policy-frameworks-and-guidance)\n  * [Documents in Legal Genres](#documents-in-legal-genres)\n\n* **Education Resources**\n  * [Comprehensive Software Examples and Tutorials](#comprehensive-software-examples-and-tutorials)\n  * [Free-ish Books](#free-ish-books)\n  * [Glossaries and Dictionaries](#glossaries-and-dictionaries)\n  * [Open-ish Classes](#open-ish-classes)\n  * [Course Syllabi](#course-syllabi)\n  * [Podcasts and Channels](#podcasts-and-channels)\n\n* **AI Incidents, Critiques, and Research Resources**\n  * [AI Incident Information Sharing Resources](#ai-incident-information-sharing-resources)\n    * [Bibliography of Papers on AI Incidents and Failures](#bibliography-of-papers-on-ai-incidents-and-failures)\n  * [AI Law, Policy, and Guidance Trackers](#ai-law-policy-and-guidance-trackers)\n  * [Challenges and Competitions](#challenges-and-competitions)\n  * [AI and Labor Resources](#ai-and-labor-resources)\n  * [Responsible and Critical Perspectives on Agentic AI](#responsible-and-critical-perspectives-on-agentic-ai)\n  * [Critiques of AI](#critiques-of-ai)\n    * [Environmental Costs of AI](#environmental-costs-of-ai)\n    * [Language Diversity and Resource Gaps](#language-diversity-and-resource-gaps)\n    * [AI Slop Genre](#ai-slop-genre)\n    * [Measurement Critiques](#measurement-critiques)\n  * [Groups and Organizations](#groups-and-organizations)\n  * [Curated Bibliographies](#curated-bibliographies)\n  * [List of Lists](#list-of-lists)\n  * [Platforms](#platforms)\n\n* **Technical Resources**\n  * [Benchmarks](#benchmarks)\n  * [Common or Useful Datasets](#common-or-useful-datasets)\n  * [Domain-specific Software](#domain-specific-software)\n  * [Machine Learning Environment Management Tools](#machine-learning-environment-management-tools)\n  * [Personal Data Protection Tools](#personal-data-protection-tools)\n  * [Open Source/Access Responsible AI Software Packages](#open-sourceaccess-responsible-ai-software-packages)\n    * [Browser](#browser)\n    * [C/C++](#cc)\n    * [JavaScript](#javascript)\n    * [Python](#python)\n    * [R](#r)\n\n* **Archived**\n  * [Archived: Official Policy, Frameworks, and Guidance](#archived-official-policy-frameworks-and-guidance)\n\n* **Citing Awesome Machine Learning Interpretability**\n  * [Citation](#citing-awesome-machine-learning-interpretability)\n\n## Community and Official Guidance Resources\n\n### Community Frameworks and Guidance\n\nThis section is for responsible ML guidance put forward by organizations or individuals, not for official government guidance.\n\n* [2024 State of the AI Regulatory Landscape](https://drive.google.com/file/d/13gyYbBixU75QwFQDTku0AMIovbeTp9_g/view)\n* [8 Principles of Responsible ML](https://ethical.institute/principles.html)\n* [A Brief Overview of AI Governance for Responsible Machine Learning Systems](https://arxiv.org/pdf/2211.13130.pdf)\n* [A checklist for auditing AI systems](https://ictinstitute.nl/a-checklist-for-auditing-ai-systems/) | ICT Institute\n* [A Digital Pandemic: Uncovering the Role of 'Yahoo Boys' in the Surge of Social Media-Enabled Financial Sextortion Targeting Minors](https://networkcontagion.us/wp-content/uploads/Yahoo-Boys_1.2.24.pdf) | Network Contagion Research Institute (NCRI), January 2024\n* [A Flexible Maturity Model for AI Governance Based on the NIST AI Risk Management Framework](https://ieeeusa.org/product/a-flexible-maturity-model-for-ai-governance/)\n* [A Guide to AI in Schools: Perspectives for the Perplexed](https://tsl.mit.edu/wp-content/uploads/2025/08/GuideToAIInSchools.pdf)\n* [A NIST Foundation To Support The Agency’s AI Mandate](https://fas.org/publication/nist-foundation/)\n* [A Primer for Developers and Policymakers](https://www.rand.org/pubs/research_reports/RRA3084-1.html)\n* [A Taxonomy of Trustworthiness for Artificial Intelligence](https://cltc.berkeley.edu/wp-content/uploads/2023/12/Taxonomy_of_AI_Trustworthiness_tables.pdf) | January 2023\n* [ABOUT ML Reference Document](https://partnershiponai.org/paper/about-ml-reference-document/)\n* [Acceptable Use Policies for Foundation Models](https://github.com/kklyman/aupsforfms) | ![](https://img.shields.io/github/stars/kklyman/aupsforfms?style=social)\n* [Access Now, Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report](https://www.accessnow.org/wp-content/uploads/2024/07/TRF-LAC-Reporte-Regional-IA-JUN-2024-V3.pdf)\n* [Adverse Event Reporting for AI: Developing the Information Infrastructure Government Needs to Learn and Act](https://hai.stanford.edu/assets/files/hai-reglab-issue-brief-adverse-event-reporting-for-ai.pdf) | July 2025\n* [AI Accidents: An Emerging Threat: What Could Happen and What to Do, CSET Policy Brief, July 2021](https://cset.georgetown.edu/wp-content/uploads/CSET-AI-Accidents-An-Emerging-Threat.pdf)\n* [AI Act Governance: Best Practices for Implementing the EU AI Act](https://www.appliedai.de/uploads/files/Best-Practices-for-Implementing-the-EU-AI-Act_2025-07-02-092027_vwvf.pdf) | Initiative for Applied Artificial Intelligence, June 2025\n* [AI Act Governance on the Ground: Canada’s Algorithmic Impact Assessment Process and Algorithm has evolved](https://www.worldprivacyforum.org/2024/08/ai-governance-on-the-ground-series-canada/)\n* [AI Act Handbook](https://www.whitecase.com/sites/default/files/2025-06/wc-eu-ai-act-handbook-finalprint.pdf) | White & Case, June 2025\n* [AI Act – Provider Only: Certification Scheme v1.5](https://forhumanity.center/site/wp-content/uploads/2025/03/Excerpt-EU-Artificial-Intelligence-Act-Provider-only-v1.5.pdf) | ForHumanity, March 2025\n* [AI Agent Governance: A Field Guide](https://static1.squarespace.com/static/64edf8e7f2b10d716b5ba0e1/t/6801438c58c2692374995db0/1744913293841/Agent+Governance_+A+Field+Guide.pdf) | (IAPS), April 2025\n* [AI alignment vs AI ethical treatment: Ten challenges](https://www.globalprioritiesinstitute.org/wp-content/uploads/Bradley-and-Saad-AI-alignment-vs-AI-ethical-treatment_-Ten-challenges.pdf) | Adam Bradley and Bradford Saad, Global Priorities Institute, July 2024\n* [AI Assurance: A Repeatable Process for Assuring AI-enabled Systems](https://www.mitre.org/sites/default/files/2024-06/PR-24-1768-AI-Assurance-A-Repeatable-Process-Assuring-AI-Enabled-Systems.pdf) | MITRE, June 2024\n* [AI Canon](https://a16z.com/ai-canon/) | Andreessen Horowitz (a16z)\n* [AI Decision-Making and the Courts: A guide for Judges, Tribunal Members, and Court Administrators](https://aija.org.au/wp-content/uploads/2023/12/AIJA_AI-DecisionMakingReport_2023update.pdf) | The Australasian Institute of Judicial Administration Inc., published June 2022 and revised and republished December 2023\n* [AI Ethics & Governance 2025: A Framework for Malaysia's Tech Industry](https://www.pikom.org.my/2025/PIKOM_AI_ethic_and_governance_2025.pdf) | PIKOM, May 2025\n* [AI Ethics and Governance in Practice](https://www.turing.ac.uk/research/research-projects/ai-ethics-and-governance-practice) | The Alan Turing Institute\n* [AI Ethics and Governance in Practice: AI Safety in Practice](https://www.turing.ac.uk/news/publications/ai-ethics-and-governance-practice-ai-safety-practice) | The Alan Turing Institute\n* [AI For a Planet Under Pressure](https://www.stockholmresilience.org/download/18.15c171219a15332ff93f68/1762500974262/AI%20for%20a%20planet%20under%20pressure_digital2.pdf) | Stockholm Resilience Centre, Stockholm University, November 5, 2025\n* [AI Governance: A Framework for Responsible and Compliant Artificial Intelligence](https://www.aigl.blog/content/files/2025/09/AI-GOVERNANCE-A-Framework-for-Responsible-and-Compliant-Artificial-Intelligence.pdf) | Sołtysiński Kawecki & Szlęzak, September 2025\n* [AI Governance Alliance Briefing Paper Series](https://www3.weforum.org/docs/WEF_AI_Governance_Alliance_Briefing_Paper_Series_2024.pdf) | World Economic Forum, January 2024\n* [AI Governance and the EU's Strategic Role in 2025](https://cadmus.eui.eu/server/api/core/bitstreams/cb201cb1-d7e1-40aa-96fb-023c5b22c22f/content) | Florence School of Transnational Governance, Marta Cantero Gamito, August 2025\n* [AI Governance InternationaL Evaluation AGILE Index 2025](https://agile-index.ai/AGILE-Index-Report-2025-EN.pdf) | July 2025\n* [AI Governance Needs Sociotechnical Expertise: Why the Humanities and Social Sciences Are Critical to Government Efforts](https://datasociety.net/wp-content/uploads/2024/05/DS_AI_Governance_Policy_Brief.pdf)\n* [AI in Africa](https://landscapestudy.tiiny.site/) | Global Center on AI Governance, AI in Africa: A Landscape Study, April 2025\n* [AI in the Public Service: From Principles to Practice](https://oxcaigg.oii.ox.ac.uk/wp-content/uploads/sites/11/2021/12/AI-in-the-Public-Service-Final.pdf) | Oxford Commission on AI & Good Governance\n* [AI Inventories: Practical Challenges for Organizational Risk Management](https://tinyurl.com/mrxrdc3y) | Responsible AI Institute and Chevron\n* [AI Liability Along the Value Chain](https://wp.table.media/wp-content/uploads/2025/04/01152117/AI-Liability-Along-the-Value-Chain_Beatriz-Arcila.pdf) | Mozilla, 2025\n* [AI Model Registries: A Foundational Tool for AI Governance](https://arxiv.org/pdf/2410.09645) | September 2024\n* [AI Model Risk Management Framework](https://cloudsecurityalliance.org/artifacts/ai-model-risk-management-framework) | Cloud Security Alliance and AI Technology and Risk Working Group, July 23, 2024\n* [AI Policy](https://taylorandfrancis.com/our-policies/ai-policy/) | Taylor & Francis\n* [AI Red-Teaming Is Not a One-Stop Solution to AI Harms: Recommendations for Using Red-Teaming for AI Accountability](https://datasociety.net/wp-content/uploads/2023/10/Recommendations-for-Using-Red-Teaming-for-AI-Accountability-PolicyBrief.pdf) | Data & Society\n* [AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies](https://www.iaps.ai/research/ai-relevant-regulatory-precedent)\n* [AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources](https://arxiv.org/pdf/2503.05780)\n* [AI Safety Governance, the Southeast Asian Way](https://www.brookings.edu/wp-content/uploads/2025/08/GS_08252025_AISA_report.pdf) | Brookings Center for Technology Innovation, AI Safety Asia (AISA), August 2025\n* [AI Safety in Practice](https://www.turing.ac.uk/sites/default/files/2024-06/aieg-ati-6-safetyv1.2.pdf) | The Alan Turing Institute\n* [AI Snake Oil](https://www.aisnakeoil.com/)\n* [AI Standards Hub](https://www.turing.ac.uk/research/research-projects/ai-standards-hub) | The Alan Turing Institute\n* [AI Sustainability Outlook: The Challenges, Potential, and Path Forward](https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/company/sustainability/salesforce-ai-sustainability-outlook.pdf) | Salesforce\n* [AI Verification](https://aiverifyfoundation.sg/what-is-ai-verify/)\n* [AI Verify Foundation](https://aiverifyfoundation.sg/what-is-ai-verify/) | AI Verify Foundation\n* [AI Won't Replace the General: Algorithms, Decision-making and Battlefield Command](https://www.turing.ac.uk/sites/default/files/2025-09/turing_final_report_ai_wont_replace_the_general_2025.pdf) | The Alan Turing Institute, September 2025\n* [AI-Generated Algorithmic Virality](https://aiforensics.org/uploads/GenAI%20Report.pdf) | AI Forensics, June 2025\n* [AI-Generated Disinformation in Europe and Africa: Use Cases, Solutions and Transnational Learning](https://www.kas.de/documents/285576/0/Study+_+AI-Generated+Disinformation+in+Europe+and+Africa+-+Ebook+%281%29.pdf/a51f9394-e955-21a1-df30-38585122303c?version=1.0&t=1739539822992) | Konrad Adenauer Stiftung, January 31, 2025\n* [AI-enabled Biosecurity: Opportunities to Strengthen U.S. Biosecurity from AI-Enabled Bioterrorism: What Policymakers Should Know](https://csis-website-prod.s3.amazonaws.com/s3fs-public/2025-08/250806_Adamson_AI-Enabled_Bioterrorism.pdf?VersionId=vS8T0bGMLr_lU7RTcXs8im_ndJuGz0cM) | Center for Strategic and International Studies, August 2025\n* [An In-Depth Guide To Help You Start Auditing Your AI Models](https://censius.ai/blogs/ai-audit-guide) | Censius\n* [An Overview of Artificial Intelligence Ethics](https://ieeexplore.ieee.org/document/9844014)\n* [An Overview of Catastrophic AI Risks](https://arxiv.org/pdf/2306.12001) | Dan Hendrycks, Mantas Mazeika, and Thomas Woodside, October 9, 2023\n* [Architectural Risk Analysis of Large Language Models](https://berryvilleiml.com/results/BIML-LLM24.pdf) | Berryville Institute of Machine Learning, requires free account\n* [Artificial Intelligence Harm and Human Rights: A High Level Exploration of the Interaction of AI Harms](https://icaad.ngo/2025/09/29/ai-harm-and-human-rights/) | ICAAD and King & Wood Mallesons, September 29, 2025\n* [Artificial Intelligence Controls Matrix Bundle](https://cloudsecurityalliance.org/artifacts/ai-controls-matrix#)\n* [Artificial Intelligence Impact Assessment](https://ecp.nl/wp-content/uploads/2018/11/Artificial-Intelligence-Impact-Assesment.pdf) | ECP Platform voor de InformatieSamenleving, November 2018\n* [Artificial Intelligence in Africa: Challenges and Opportunities](https://www.policycenter.ma/sites/default/files/2024-09/PB_23_24%20%28Azeroual%29%20%28EN%29.pdf) | Policy Center for the New South, Fahd Azaroual, May 2024\n* [Artificial Intelligence in the Securities Industry](https://www.finra.org/sites/default/files/2020-06/ai-report-061020.pdf) | Financial Industry Regulatory Authority\n* [Artificial Intelligence Tools Versus Practice in Conflict Prediction: The Case of Mali](https://hcss.nl/wp-content/uploads/2020/04/Artificial-Intelligence-Tools-Versus-Practice-in-Conflict-Prediction-The-Case-of-Mali.pdf) | The Hague Centre for Strategic Studies, April 29, 2020\n* [Assessing AI: Surveying the Spectrum of Approaches to Understanding and Auditing AI Systems](https://cdt.org/wp-content/uploads/2025/01/2025-01-15-CDT-AI-Gov-Lab-Auditing-AI-report.pdf) | Center for Democracy and Technology (CDT), January 2025\n* [Assessing the Implementation of Federal AI Leadership and Compliance Mandates](https://hai.stanford.edu/sites/default/files/2025-01/HAI-RegLab-White-Paper-Federal-AI-Leadership-and-Compliance.pdf) | Stanford University Human-Centered Artificial Intelligence (HAI)\n* [Auditing Artificial Intelligence](https://ec.europa.eu/futurium/en/system/files/ged/auditing-artificial-intelligence.pdf) | ISACA\n* [Auditing Guidelines for Artificial Intelligence](https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2020/volume-26/auditing-guidelines-for-artificial-intelligence) | ISACA\n* [Auditing machine learning algorithms: A white paper for public auditors](https://www.auditingalgorithms.net/index.html)\n* [AuditBoard: 5 AI Auditing Frameworks to Encourage Accountability](https://www.auditboard.com/blog/ai-auditing-frameworks/)\n* [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) | Microsoft\n* [Building an early warning system for LLM-aided biological threat creation](https://openai.com/research/building-an-early-warning-system-for-llm-aided-biological-threat-creation) | OpenAI\n* [Brendan Bycroft's LLM Visualization](https://bbycroft.net/llm)\n* [C2PA: Coalition for Content Provenance and Authenticity](https://c2pa.org/) | (C2PA)\n* [Capability Maturity Model Integration Resources](https://cmmiinstitute.com/) | ISACA\n* [Casey Flores, AIGP Study Guide](https://www.linkedin.com/feed/update/urn:li:activity:7201048113090809856?utm_source=share&utm_medium=member_desktop)\n* [Cataloguing LLM Evaluations](https://aiverifyfoundation.sg/downloads/Cataloguing_LLM_Evaluations.pdf) | AI Verify Foundation, Infocomm Media Development Authority (Singapore) and AI Verify Foundation, October 2023\n* [CEN-CENELEC JTC21 AI Standards: Complete Detailed Overview](https://jtc21.eu/wp-content/uploads/2025/06/CEN-CENELEC-JTC21-AI-Standards-Complete-Detailed-Overview.pdf)\n* [Center for AI and Digital Policy Reports](https://www.caidp.org/reports/)\n* [Center for Countering Digital Hate, Fake Friend: How ChatGPT betrays vulnerable teens by encouraging dangerous behavior](https://counterhate.com/wp-content/uploads/2025/08/Fake-Friend_CCDH_FINAL-public.pdf) | Center for Countering Digital Hate, 2025\n* [Center for Countering Digital Hate, YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf) | Center for Countering Digital Hate (CCDH)\n* [Center for Democracy and Technology, AI Policy & Governance](https://cdt.org/area-of-focus/ai-policy-governance/)\n* [Center for Democracy and Technology, Applying Sociotechnical Approaches to AI Governance in Practice](https://cdt.org/insights/applying-sociotechnical-approaches-to-ai-governance-in-practice/)\n* [Center for Democracy and Technology, In Deep Trouble: Surfacing Tech-Powered Sexual Harassment in K-12 Schools](https://cdt.org/insights/report-in-deep-trouble-surfacing-tech-powered-sexual-harassment-in-k-12-schools/)\n* [Center for Security and Emerging Technology, Adding Structure to AI Harm: An Introduction to CSET's AI Harm Framework](https://cset.georgetown.edu/publication/adding-structure-to-ai-harm/)\n* [Center for Security and Emerging Technology, AI Incident Collection: An Observational Study of the Great AI Experiment](https://cset.georgetown.edu/publication/ai-incident-collection-an-observational-study-of-the-great-ai-experiment/)\n* [Center for Security and Emerging Technology, Chinese Critiques of Large Language Models: Finding the Path to General Intelligence](https://cset.georgetown.edu/wp-content/uploads/CSET-Chinese-Critiques-of-Large-Language-Models-Finding-the-Path-to-General-Artificial-Intelligence.pdf) | January 2025\n* [Center for Security and Emerging Technology, CSET Publications](https://cset.georgetown.edu/publications/)\n* [Center for Security and Emerging Technology, Putting Explainable AI to the Test: A Critical Look at AI Evaluation Approaches](https://cset.georgetown.edu/wp-content/uploads/CSET-Putting-Explainable-AI-to-the-Test.pdf) | February 2025\n* [Center for Security and Emerging Technology, Repurposing the Wheel: Lessons for AI Standards](https://cset.georgetown.edu/publication/repurposing-the-wheel/)\n* [Center for Security and Emerging Technology, Translating AI Risk Management Into Practice](https://cset.georgetown.edu/article/translating-ai-risk-management-into-practice/)\n* [Center for Security and Emerging Technology, Understanding AI Harms: An Overview](https://cset.georgetown.edu/article/understanding-ai-harms-an-overview/)\n* [Character Flaws: School Shooters, Anorexia Coaches, and Sexualized Minors: A Look at Harmful Character Chatbots and the Communities That Build Them](https://public-assets.graphika.com/reports/graphika-report-character-flaws.pdf) | Graphika Atlas Report, March 2025\n* [Children & AI Design Code: A Protocol for the development and use of AI systems that impact children](https://5rightsfoundation.com/wp-content/uploads/2025/03/5rights_AI_CODE_DIGITAL.pdf)\n* [Chinese Critiques of Large Language Models: Finding the Path to General Intelligence](https://cset.georgetown.edu/wp-content/uploads/CSET-Chinese-Critiques-of-Large-Language-Models-Finding-the-Path-to-General-Artificial-Intelligence.pdf) | CSET, January 2025\n* [Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing](https://dl.acm.org/doi/abs/10.1145/3351095.3372873)\n* [Cloud Security Alliance, Artificial Intelligence Controls Matrix Bundle](https://cloudsecurityalliance.org/artifacts/ai-controls-matrix#)\n* [Cloud Security Alliance, AI Model Risk Management Framework](https://cloudsecurityalliance.org/artifacts/ai-model-risk-management-framework) | AI Technology and Risk Working Group, July 23, 2024\n* [Coalition for Content Provenance and Authenticity](https://c2pa.org/) | (C2PA)\n* [Countries With Draft AI Legislation or Frameworks](https://dominiquesheltonleipzig.com/country-legislation-frameworks/) | Dominique Shelton Leipzig\n* [Data Governance in the Cloud - part 2 - Tools](https://cloud.google.com/blog/products/data-analytics/data-governance-in-the-cloud-part-2-tools) | Google\n* [Data Provenance Explorer](https://www.dataprovenance.org/)\n* [Data governance in the cloud - part 1 - People and processes](https://cloud.google.com/blog/products/data-analytics/data-governance-and-operating-model-for-analytics-pt1) | Google\n* [Data Statements](https://techpolicylab.uw.edu/data-statements/) | University of Washington Tech Policy Lab\n* [Data Stewardship in Practice](https://www.turing.ac.uk/sites/default/files/2024-06/aieg-ati-5-datastewardshipv1.2.pdf) | The Alan Turing Institute\n* [Data governance in AI: Mapping governance](https://theodi.org/insights/reports/understanding-data-governance-in-ai-mapping-governance/) | Open Data Institute\n* [Data Privacy FAQ](https://aws.amazon.com/compliance/data-privacy-faq/) | AWS\n* [Data Use Policy](https://ourdataourselves.tacticaltech.org/data-use-policy/) | Our Data Our Selves, Tactical Tech\n* [Debugging Machine Learning Models](https://debug-ml-iclr2019.github.io/) | ICLR workshop proceedings\n* [Decision Points in AI Governance: Three Case Studies Explore Efforts to Operationalize AI Principles](https://cltc.berkeley.edu/wp-content/uploads/2020/05/Decision_Points_AI_Governance.pdf) | University of California, Berkeley, Center for Long-Term Cybersecurity\n* [Deepfake Pornography Goes to Washington: Measuring the Prevalence of AI-Generated Non-Consensual Intimate Imagery Targeting Congress](https://static1.squarespace.com/static/6612cbdfd9a9ce56ef931004/t/67586997eaec5c6ae3bb5e24/1733847451191/ASP+DFP+Report.pdf) | American Sunlight Project, December 11, 2024\n* [Dealing with Bias and Fairness in AI/ML/Data Science Systems](https://docs.google.com/presentation/d/17o_NzplYua5fcJFuGcy1V1-5GFAHk7oHAF4dN44NkUE)\n* [Demos, AI – Trustworthy By Design: How to build trust in AI systems, the institutions that create them and the communities that use them](https://demos.co.uk/research/ai-trustworthy-by-design-how-to-build-trust-in-ai-systems-the-institutions-that-create-them-and-the-communities-that-use-them/)\n* [Digital Policy Alert, The Anatomy of AI Rules: A systematic comparison of AI rules across the globe](https://digitalpolicyalert.org/ai-rules/the-anatomy-of-AI-rules)\n* [Disrupting malicious uses of AI: June 2025](https://cdn.openai.com/threat-intelligence-reports/5f73af09-a3a3-4a55-992e-069237681620/disrupting-malicious-uses-of-ai-june-2025.pdf) | OpenAI\n* [Distill](https://distill.pub)\n* [Doing AI Differently: Rethinking the foundations of AI via the humanities](https://www.turing.ac.uk/sites/default/files/2025-07/doing_ai_differently_white_paper.pdf) | Alan Turing Institute, July 31, 2025\n* [Emotional Manipulation by AI Companions](https://www.hbs.edu/ris/Publication%20Files/26005_951004f6-0b0b-432b-846a-5f95c103d07c.pdf) | Harvard Business School, 2025\n* [Evaluating social and ethical risks from generative AI](https://deepmind.google/discover/blog/evaluating-social-and-ethical-risks-from-generative-ai/) | Google\n* [Evidence of CCP Censorship, Propaganda in U.S. LLM Responses](https://cdn.prod.website-files.com/67919c3b2972e57c613c2ea2/685b1a27a830fb5b6e7ff511_Sentinel%20Brief%20-%20Evidence%20of%20CCP%20Censorship%20in%20LLM%20Responses.pdf) | Sentinel Brief\n* [Explainable AI in Finance: Addressing the Needs of Diverse Stakeholders](https://rpc.cfainstitute.org/sites/default/files/docs/research-reports/wilson_explainableaiinfinance_online.pdf) | Cheryll-Ann Wilson, CFA Institute, Research & Policy Center, August 2025\n* [Fairly's Global AI Regulations Map](https://github.com/fairlyAI/global-ai-regulations-map/blob/dev/README.md) | ![](https://img.shields.io/github/stars/fairlyAI/global-ai-regulations-map?style=social)\n* [Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey](https://dl.acm.org/doi/10.1145/3696457)\n* [FATML Principles and Best Practices](https://www.fatml.org/resources/principles-and-best-practices)\n* [Federation of American Scientists, A NIST Foundation To Support The Agency’s AI Mandate](https://fas.org/publication/nist-foundation/)\n* [First of its kind Generative AI Evaluation Sandbox for Trusted AI by AI Verify Foundation and IMDA](https://www.imda.gov.sg/resources/press-releases-factsheets-and-speeches/press-releases/2023/generative-ai-evaluation-sandbox) | Infocomm Media Development Authority (Singapore)\n* [ForHumanity Body of Knowledge](https://forhumanity.center/bok/)\n* [Forging Global Cooperation on AI Risks: Cyber Policy as a Governance Blueprint](https://parispeaceforum.org/app/uploads/2025/02/forging-global-cooperation-on-ai-risks-cyber-policy-as-a-governance-blueprint.pdf) | Paris Peace Forum, February 2025\n* [Framework for Identifying Highly Consequential AI Use Cases](https://www.scsp.ai/wp-content/uploads/2023/11/SCSP_JHU-HCAI-Framework-Nov-6.pdf) | Special Competitive Studies Project and Johns Hopkins University Applied Physics Laboratory\n* [From Principles to Practice: An interdisciplinary framework to operationalise AI ethics](https://www.ai-ethics-impact.org/resource/blob/1961130/c6db9894ee73aefa489d6249f5ee2b9f/aieig---report---download-hb-data.pdf)\n* [Gage Repeatability and Reproducibility](https://asq.org/quality-resources/gage-repeatability)\n* [Gen-AI: Artificial Intelligence and the Future of Work](https://www.imf.org/-/media/Files/Publications/SDN/2024/English/SDNEA2024001.ashx) | International Monetary Fund\n* [Generative AI: A New Threat for Online Child Sexual Exploitation and Abuse](https://unicri.org/sites/default/files/2024-09/Generative-AI-New-Threat-Online-Child-Abuse.pdf) | United Nations Interregional Crime and Justice Research Institute (UNICRI)  Centre for AI and Robotics, Bracket Foundation, and Value for Good, September 2024\n* [Generative AI Prohibited Use Policy](https://policies.google.com/terms/generative-ai/use-policy) | Google\n* [Generative AI Vendor Risk Assessment Guide](https://www.fsisac.com/hubfs/Knowledge/AI/FSISAC_GenerativeAI-VendorEvaluation&QualitativeRiskAssessment.pdf) | Future Society, FS-ISAC, February 2024,\n* [Global AI Governance Law and Policy: Canada, EU, Singapore, UK and US](https://iapp.org/media/pdf/resource_center/global_ai_governance_law_policy_series.pdf) | IAPP\n* [Guide for Australian Business: Understanding 42001](https://cdn.prod.website-files.com/6420f704f2602a2ee7f79d26/662aefb77b3077382ff25eef_understanding%2042001%20ai%20management%20system%20standard%20whitepaper.pdf) | Standards Australia and National Artificial Intelligence Centre\n* [Guide for Preparing and Responding to Deepfake Events: From the OWASP Top 10 for LLM Applications Team](https://genai.owasp.org/resource/guide-for-preparing-and-responding-to-deepfake-events/) | OWASP, Version 1, September 2024\n* [Guidance for Safe Foundation Model Deployment: A Framework for Collective Action](https://partnershiponai.org/modeldeployment/)\n* [Guidelines for AI in parliaments](https://www.ipu.org/file/20632/download) | Inter-Parliamentary Union, December 2024\n* [Guidelines on the Application of the Definition of an AI System in the AI Act: ELI Proposal for a Three-Factor Approach](https://www.europeanlawinstitute.eu/fileadmin/user_upload/p_eli/Publications/ELI_Response_on_the_definition_of_an_AI_System.pdf) | European Law Institute, Response of the ELI to the EU Commission's Consultation, November 1, 2024\n* [HackerOne Blog](https://www.hackerone.com/vulnerability-and-security-testing-blog)\n* [How Can We Tackle AI-Fueled Misinformation and Disinformation in Public Health?](https://www.bu.edu/ceid/2024/04/25/how-can-we-tackle-ai-fueled-misinformation-and-disinformation-in-public-health/) | Brown University\n* [How do I cite generative AI in MLA style?](https://style.mla.org/citing-generative-ai/) | MLA\n* [How Microsoft names threat actors](https://learn.microsoft.com/en-us/unified-secops-platform/microsoft-threat-actor-naming) | Microsoft\n* [How People Around the World View AI](https://www.pewresearch.org/wp-content/uploads/sites/20/2025/10/pg_2025.10.15_ai_report.pdf) | Pew Research Center, October 15, 2025\n* [How to Perform an AI Audit for UK Organisations](https://www.haptic-networks.com/cyber-security/how-to-perform-an-ai-audit/) | Haptic Networks\n* [Human-Calibrated Automated Testing and Validation of Generative Language Models: An Overview](https://arxiv.org/pdf/2411.16391) | Agus Sudjianto, Aijun Zhang, Srinivas Neppalli, Tarun Joshi, and Michael Malohlava, December 7, 2024\n* [Identifying and Overcoming Common Data Mining Mistakes](https://support.sas.com/resources/papers/proceedings/proceedings/forum2007/073-2007.pdf)\n* [Implementing the AI Act in Belgium: Scope of Application and Authorities](https://data-en-maatschappij.ai/uploads/Policy-brief-Implementing-the-AI-act-in-Belgium_2024-12-23-115650_shpg.pdf) | Data & Society Knowledge Centre, December 2024\n* [Independent Audit of AI Systems](https://forhumanity.center/independent-audit-of-ai-systems/) | ForHumanity\n* [Information System Contingency Planning Guidance](https://www.isaca.org/resources/isaca-journal/issues/2021/volume-3/information-system-contingency-planning-guidance) | Larry G. Wlosinski, April 30, 2021\n* [Institute for AI Policy and Strategy](https://www.iaps.ai/ourresearch) | (IAPS)\n* [Institute of Internal Auditors](https://www.theiia.org/en/pages/search-results/?keyword=artificial+intelligence)\n* [Intolerable Risk Threshold Recommendations for Artificial Intelligence: Key Principles, Considerations, and Case Studies to Inform Frontier AI Safety Frameworks for Industry and Government](https://cltc.berkeley.edu/wp-content/uploads/2025/02/Intolerable-Risk-Threshold-Recommendations-for-Artificial-Intelligence.pdf) | University of California, Berkeley, Center for Long-Term Cybersecurity, February 2025\n* [International AI Safety Report](https://internationalaisafetyreport.org/sites/default/files/2025-10/first-key-update_0.pdf) | First Key Update, Capabilities and Risk Implications, October 2025\n* [International Bar Association, The Future Is Now: Artificial Intelligence and the Legal Profession](https://www.ibanet.org/document?id=The-future-is%20now-AI-and-the-legal-profession-report) | International Bar Association and the Center for AI and Digital Policy\n* [International Monetary Fund, Gen-AI: Artificial Intelligence and the Future of Work](https://www.imf.org/-/media/Files/Publications/SDN/2024/English/SDNEA2024001.ashx)\n* [International Organization for Standardization, ISO/IEC 42001:2023, Information technology — Artificial intelligence — Management system](https://www.iso.org/standard/81230.html)\n* [ISO policy brief: Harnessing international standards for responsible AI development and governance](https://www.iso.org/files/live/sites/isoorg/files/publications/en/PUB100498.pdf) | ISO, 2025\n* [ITI's AI Security Policy Principles](https://www.itic.org/documents/artificial-intelligence/ITI_AI-Security-Principles_102124_FINAL.pdf) | Information Technology Industry (ITI) Council, October 2024\n* [Just Security's Artificial Intelligence Archive](https://www.justsecurity.org/99958/just-securitys-artificial-intelligence-archive/)\n* [Key Considerations When Using Artificial Intelligence in the Public Sector](https://www.aaas.org/sites/default/files/2025-01/Key%20Considerations%20AI%20for%20Public%20Sector.pdf) | EPI Center and AAAS, February 2025\n* [Know Your Data](https://knowyourdata.withgoogle.com/) | Google\n* [Language Model Risk Cards: Starter Set](https://github.com/leondz/lm_risk_cards) | ![](https://img.shields.io/github/stars/leondz/lm_risk_cards?style=social)\n* [Large language models explained with a minimum of math and jargon](https://www.understandingai.org/p/large-language-models-ed-with)\n* [LC Labs AI Planning Framework](https://github.com/LibraryOfCongress/labs-ai-framework) | ![](https://img.shields.io/github/stars/LibraryOfCongress/labs-ai-framework?style=social), Library of Congress\n* [Learning from other domains to advance AI evaluation and testing](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/08/Learning-from-other-Domains-to-Advance-AI-Evaluation-and-Testing_-v3-1.pdf) | Microsoft\n* [Learning from other domains to advance AI evaluation and testing](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/08/Learning-from-other-Domains-to-Advance-AI-Evaluation-and-Testing_-v3-1.pdf) | Microsoft, August 2025\n* [Llama 2 Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/)\n* [LLM Visualization](https://bbycroft.net/llm)\n* [Machine Learning Quick Reference: Algorithms](https://support.sas.com/rnd/app/data-mining/enterprise-miner/pdfs/Machine_Learning_Quick_Ref_Algorithms_Mar2017.pdf)\n* [Machine Learning Quick Reference: Best Practices](https://support.sas.com/rnd/app/data-mining/enterprise-miner/pdfs/Machine_Learning_Quick_Ref_Best_Practices.pdf)\n* [Manifest MLBOM Wiki](https://github.com/manifest-cyber/mlbom)\n* [Map of Practices: AutoPractices](https://findresearcher.sdu.dk/ws/portalfiles/portal/295221640/AutoPractices_Map_WEB.pdf) | Governing AI Technologies in Military Systems from the Bottom Up: Practices to Sustain and Strengthen Human Agency, September 2025, The AutoPractices Project. Odense: Center for War Studies\n* [Mapping AI Risk Mitigations: Evidence Scan and Draft Mitigation Taxonomy](https://cdn.prod.website-files.com/669550d38372f33552d2516e/6887e58496902e3bcad04a5a_Mapping%20AI%20Risk%20Mitigations.pdf) | MIT AI Risk Index, FutureTech, and MIT, July 2025\n* [Mapping Technical Safety Research at AI Companies: A literature review and incentives analysis](https://arxiv.org/pdf/2409.07878) | (IAPS)\n* [MIT AI Risk-Management Standards Profile for General-Purpose AI and Foundation Models](https://cltc.berkeley.edu/wp-content/uploads/2025/01/Berkeley-AI-Risk-Management-Standards-Profile-for-General-Purpose-AI-and-Foundation-Models-v1-1.pdf) | University of California, Berkeley, Center for Long-Term Cybersecurity, January 2025\n* [Mitigating the risk of generative AI models creating Child Sexual Abuse Materials: An analysis by child safety nonprofit Thorn](https://partnershiponai.org/wp-content/uploads/2024/11/case-study-thorn.pdf) | Partnership on AI and Thorn\n* [Model Transparency Ratings](https://aimodelratings.com/) | Trustible\n* [model-cards-and-datasheets](https://github.com/ivylee/model-cards-and-datasheets) | ![](https://img.shields.io/github/stars/ivylee/model-cards-and-datasheets?style=social)\n* [Multi-Agent Risks from Advanced AI](https://www.cs.toronto.edu/~nisarg/papers/Multi-Agent-Risks-from-Advanced-AI.pdf) | Cooperative AI Foundation, February 2025\n* [Navigating AI Compliance Part 1 Tracing Failure Patterns in History](https://securityandtechnology.org/wp-content/uploads/2024/12/Navigating-AI-Compliance.pdf) | Institute for Security and Technology (IST), December 2024\n* [Navigating AI Compliance Part 2 Risk Mitigation Strategies for Safeguarding Against Future Failures](https://securityandtechnology.org/wp-content/uploads/2025/03/Navigating-AI-Compliance-Part-2-Risk-Mitigation-Strategies-for-Safeguarding-Against-Future-Failures.pdf) | Institute for Security and Technology (IST), March 2025\n* [Navigating the AI Frontier: A Primer on the Evolution and Impact of AI Agents](https://reports.weforum.org/docs/WEF_Navigating_the_AI_Frontier_2024.pdf) | World Economic Forum and Capgemini, December 2024\n* [NewsGuard AI Tracking Center](https://www.newsguardtech.com/special-reports/ai-tracking-center/)\n* [News Integrity in AI Assistants: An international PSM study](https://www.ebu.ch/Report/MIS-BBC/NI_AI_2025.pdf) | EBU and BBC, October 2025\n* [On Risk Assessment and Mitigation for Algorithmic Systems](https://drive.google.com/file/d/1ZMt7igUcKUq00yakCnbxBCcaA7vajAix/view) | Integrity Institute Report, February 2024\n* [Open Problems in Technical AI Governance: A repository of open problems in technical AI governance](https://taig.stanford.edu/)\n* [Open Sourcing Highly Capable Foundation Models](https://www.governance.ai/research-paper/open-sourcing-highly-capable-foundation-models)\n* [Opportunities to Strengthen U.S. Biosecurity from AI-Enabled Bioterrorism: What Policymakers Should Know](https://csis-website-prod.s3.amazonaws.com/s3fs-public/2025-08/250806_Adamson_AI-Enabled_Bioterrorism.pdf?VersionId=vS8T0bGMLr_lU7RTcXs8im_ndJuGz0cM) | Center for Strategic and International Studies, August 2025\n* [Organization and Training of a Cyber Security Team](http://ieeexplore.ieee.org/document/1245662)\n* [Our Data Our Selves, Data Use Policy](https://ourdataourselves.tacticaltech.org/data-use-policy/)\n* [OWASP AI Testing Guide](https://owasp.org/www-project-ai-testing-guide/)\n* [PAIR Explorables: Datasets Have Worldviews](https://pair.withgoogle.com/explorables/dataset-worldviews/)\n* [People + AI Guidebook](https://pair.withgoogle.com/guidebook/) | PAIR Guidebook\n* [Perspectives on Issues in AI Governance](https://ai.google/static/documents/perspectives-on-issues-in-ai-governance.pdf) | Google\n* [Policy Center for the New South, Artificial Intelligence in Africa: Challenges and Opportunities](https://www.policycenter.ma/sites/default/files/2024-09/PB_23_24%20%28Azeroual%29%20%28EN%29.pdf) | Fahd Azaroual, May 2024\n* [Privacy Notice](https://aws.amazon.com/privacy/) | AWS\n* [PwC's Responsible AI](https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai.html)\n* [RAND Corporation, A Primer for Developers and Policymakers](https://www.rand.org/pubs/research_reports/RRA3084-1.html)\n* [RAND Corporation, Analyzing Harms from AI-Generated Images and Safeguarding Online Authenticity](https://www.rand.org/pubs/perspectives/PEA3131-1.html)\n* [RAND Corporation, Strengthening Emergency Preparedness and Response for AI Loss of Control Incidents](https://www.rand.org/content/dam/rand/pubs/research_reports/RRA3800/RRA3847-1/RAND_RRA3847-1.pdf) | RAND Europe, July 30, 2025\n* [RAND Corporation, US Tort Liability for Large-Scale Artificial Intelligence Damages, A Primer for Developers and Policymakers](https://www.rand.org/pubs/research_reports/RRA3084-1.html)\n* [Raising Standards: Data and Artificial Intelligence in Southeast Asia](https://asiasociety.org/sites/default/files/inline-files/ASPI_RaisingStandards_report_fin_web_0.pdf) | Asia Society Policy Institute, Elina Noor and Mark Bryan Manantan, July 2022\n* [Ravit Dotan's Projects](https://www.techbetter.ai/projects-1)\n* [Real People in Fake Porn: How a Federal Right of Publicity Could Assist in the Regulation of Deepfake Pornography](https://www.americanbar.org/content/dam/aba/publications/Jurimetrics/spring-2024/real-people-in-fake-porn-how-a-federal-right-of-publicity-could-assist-in-the-regulation-of-deepfake-pornography.pdf)\n* [Recommendations for the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance](https://drive.google.com/file/d/17mBzqt7foXThI9xcAP8gsTKan34Zk5Mv/view) | Simon Institute for Longterm Governance, February 2025\n* [Regulating Under Uncertainty: Governance Options for Generative AI](https://fsi9-prod.s3.us-west-1.amazonaws.com/s3fs-public/2024-12/GenAI_Report_REV_Master_%20as%20of%20Dec%2012.pdf) | Stanford Cyber Policy Center, Florence G'Sell, September 2024\n* [Responsible AI at Stanford: Enabling innovation through AI best practices](https://uit.stanford.edu/security/responsibleai)\n* [Responsible Data Stewardship in Practice](https://www.turing.ac.uk/sites/default/files/2024-06/aieg-ati-5-datastewardshipv1.2.pdf) | The Alan Turing Institute\n* [Responsible Enterprise AI in the Agentic Era](https://www.infosys.com/iki/documents/responsible-enterprise-ai-agentic-era.pdf) | Infosys\n* [Responsible Practices for Synthetic Media: A Framework for Collective Action](https://syntheticmedia.partnershiponai.org/)\n* [Risk Taxonomy and Thresholds for Frontier AI Frameworks](https://www.frontiermodelforum.org/uploads/2025/06/FMF-Technical-Report-on-Frontier-Risk-Taxonomy-and-Thresholds.pdf) | Frontier Model Forum, June 18, 2025\n* [Risk Tiers: Towards a Gold Standard for Advanced AI](https://aigi.ox.ac.uk/wp-content/uploads/2025/06/AIGI-gold-standard-risk-tiers-convening.pdf) | AI Governance Initiative, Oxford Martin School, and the University of Oxford, June 2025\n* [Safe and Reliable Machine Learning](https://www.dropbox.com/s/sdu26h96bc0f4l7/FAT19-AI-Reliability-Final.pdf?dl=0)\n* [Sample AI Incident Response Checklist](https://bnh-ai.github.io/resources/)\n* [SHRM Generative Artificial Intelligence AI Chatbot Usage Policy](https://www.shrm.org/resourcesandtools/tools-and-samples/policies/pages/chatgpt-generative-ai-usage.aspx)\n* [Sovereign AI and Sustainable Computation for Indigenous Communities](https://iem.ucsd.edu/_files/GEMSTONES-FOX-ZGPUS-PLUS-BIOSKETCH-031225.pdf) | Keolu Fox\n* [State of Agentic AI Security and Governance: OWASP Gen AI Security Project Agentic Security Initiative](https://genai.owasp.org/download/50592/?tmstv=1754459367) | Version 1.0, July 2025\n* [State of AI Safety in China](https://concordia-ai.com/wp-content/uploads/2025/07/State-of-AI-Safety-in-China-2025.pdf) | Concordia AI, July 2025\n* [Std 1012-1998 Standard for Software Verification and Validation](https://people.eecs.ku.edu/~hossein/Teaching/Stds/1012.pdf)\n* [Summary Report: Workshop on the Geopolitics of Critical Minerals and the AI Supply Chain](https://www.ias.edu/sites/default/files/Critical-Minerals-Workshop_Summary-Report.pdf) | Institute for Advanced Study, August 2025\n* [Synthetic Data: The New Data Frontier](https://reports.weforum.org/docs/WEF_Synthetic_Data_2025.pdf) | World Economic Forum, September 2025\n* [System cards](https://ai.meta.com/tools/system-cards/) | Meta\n* [Taskade: AI Audit PBC Request Checklist Template](https://www.taskade.com/templates/engineering/audit-pbc-request-checklist)\n* [Taxonomy of Failure Mode in Agentic AI Systems](https://cdn-dynmedia-1.microsoft.com/is/content/microsoftcorp/microsoft/final/en-us/microsoft-brand/documents/Taxonomy-of-Failure-Mode-in-Agentic-AI-Systems-Whitepaper.pdf) | Microsoft\n* [Technology Trends Outlook 2025](https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20top%20trends%20in%20tech%202025/mckinsey-technology-trends-outlook-2025.pdf) | McKinsey & Company, July 2025, Fifth Edition\n* [Tech Policy Press - Artificial Intelligence](https://www.techpolicy.press/category/artificial-intelligence/)\n* [TechTarget: 9 questions to ask when auditing your AI systems](https://www.techrepublic.com/article/9-questions-to-ask-when-auditing-your-ai-systems/)\n* [The AI Act between Digital and Sectoral Regulations](https://www.bertelsmann-stiftung.de/fileadmin/files/user_upload/The_AI_Act_between_Digital_and_Sectoral_Regulations__2024_en.pdf) | Bertelsmann Stiftung, December 2024\n* [The AI Act is coming: EU reaches political agreement on comprehensive regulation of artificial intelligence](https://www.engage.hoganlovells.com/knowledgeservices/news/the-ai-act-is-coming-eu-reaches-political-agreement-on-comprehensive-regulation-of-artificial-intelligence?nav=FRbANEucS95NMLRN47z%2BeeOgEFCt8EGQ71hKXzqW2Ec%3D&key=BcJlhLtdCv6%2FJTDZxvL23TQa3JHL2AIGr93BnQjo2SkGJpG9xDX7S2thDpAQsCconWHAwe6cJTmX%2FZxLGrXbZz2L%2BEiiz68X&uid=iZAX%2FROFT6Q%3D) | Hogan Lovells\n* [The Complete Guide to Crowdsourced Security Testing, Government Edition](https://www.synack.com/wp-content/uploads/2022/09/Crowdsourced-Security-Landscape-Government.pdf) | Synack\n* [The Ethics of AI Ethics: An Evaluation of Guidelines](https://link.springer.com/content/pdf/10.1007/s11023-020-09517-8.pdf)\n* [The Ethics of Developing, Implementing, and Using Advanced Warehouse Technologies: Top-Down Principles Versus The Guidance Ethics Approach](https://journals.open.tudelft.nl/jhtr/article/view/7098/6136)\n* [The Foundation Model Transparency Index](https://crfm.stanford.edu/fmti/)\n* [The Future Is Now: Artificial Intelligence and the Legal Profession](https://www.ibanet.org/document?id=The-future-is%20now-AI-and-the-legal-profession-report) | International Bar Association and the Center for AI and Digital Policy\n* [The Implications of Artificial Intelligence in Cybersecurity: Shifting the Offense-Defense Balance](https://securityandtechnology.org/virtual-library/reports/the-implications-of-artificial-intelligence-in-cybersecurity/) | Institute for Security and Technology (IST)\n* [The Landscape of ML Documentation Tools](https://huggingface.co/docs/hub/model-card-landscape-analysis) | Hugging Face\n* [The Rise of Generative AI and the Coming Era of Social Media Manipulation 3.0: Next-Generation Chinese Astroturfing and Coping with Ubiquitous AI](https://www.rand.org/pubs/perspectives/PEA2679-1.html)\n* [Towards Effective Governance of Foundation Models and Generative AI](https://thefuturesociety.org/towards-effective-governance-of-foundation-models-and-generative-ai/) | Future Society\n* [Toward an evaluation science for generative AI systems](https://arxiv.org/pdf/2503.05336)\n* [Towards Traceability in Data Ecosystems using a Bill of Materials Model](https://arxiv.org/pdf/1904.04253.pdf) | Manifest MLBOM Wiki\n* [Transformed by AI: How Generative Artificial Intelligence Could Affect Work in the UK—And How to Manage It](https://ippr-org.files.svdcdn.com/production/Downloads/Transformed_by_AI_March24_2024-03-27-121003_kxis.pdf) | Institute for Public Policy Research (IPPR)\n* [Troubleshooting Deep Neural Networks](http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf)\n* [Trustible, Enhancing the Effectiveness of AI Governance Committees](https://www.trustible.ai/post/enhancing-the-effectiveness-of-ai-governance-committees)\n* [Twitter Algorithmic Bias Bounty](https://hackerone.com/twitter-algorithmic-bias?type=team)\n* [Understanding data governance in AI: Mapping governance](https://theodi.org/insights/reports/understanding-data-governance-in-ai-mapping-governance/) | Open Data Institute\n* [Unite.AI: How to perform an AI Audit in 2023](https://www.unite.ai/how-to-perform-an-ai-audit-in-2023/)\n* [University of California, Berkeley, Center for Long-Term Cybersecurity, AI Risk-Management Standards Profile for General-Purpose AI and Foundation Models](https://cltc.berkeley.edu/wp-content/uploads/2025/01/Berkeley-AI-Risk-Management-Standards-Profile-for-General-Purpose-AI-and-Foundation-Models-v1-1.pdf) | Version 1.1, January 2025\n* [University of California, Berkeley, Center for Long-Term Cybersecurity, Decision Points in AI Governance: Three Case Studies Explore Efforts to Operationalize AI Principles](https://cltc.berkeley.edu/wp-content/uploads/2020/05/Decision_Points_AI_Governance.pdf)\n* [University of California, Berkeley, Center for Long-Term Cybersecurity, Intolerable Risk Threshold Recommendations for Artificial Intelligence: Key Principles, Considerations, and Case Studies to Inform Frontier AI Safety Frameworks for Industry and Government](https://cltc.berkeley.edu/wp-content/uploads/2025/02/Intolerable-Risk-Threshold-Recommendations-for-Artificial-Intelligence.pdf) | February 2025\n* [University of California, Berkeley, Information Security Office, How to Write an Effective Website Privacy Statement](https://security.berkeley.edu/how-write-effective-website-privacy-statement)\n* [University of Washington Tech Policy Lab, Data Statements](https://techpolicylab.uw.edu/data-statements/)\n* [US Open-Source AI Governance: Balancing Ideological and Geopolitical Considerations with China Competition](https://cdn.prod.website-files.com/65af2088cac9fb1fb621091f/67aaca031ed677c879434284_Final_US%20Open-Source%20AI%20Governance.pdf) | Center for AI Policy, February 2025\n* [Warning Signs: The Future of Privacy and Security in an Age of Machine Learning](https://fpf.org/wp-content/uploads/2019/09/FPF_WarningSigns_Report.pdf)\n* [What Are High-Risk AI Systems Within the Meaning of the EU’s AI Act, and What Requirements Apply to Them?](https://www.wilmerhale.com/en/insights/blogs/wilmerhale-privacy-and-cybersecurity-law/20240717-what-are-highrisk-ai-systems-within-the-meaning-of-the-eus-ai-act-and-what-requirements-apply-to-them) | WilmerHale\n* [When Not to Trust Your Explanations](https://docs.google.com/presentation/d/10a0PNKwoV3a1XChzvY-T1mWudtzUIZi3sCMzVwGSYfM/edit)\n* [Who Should Develop Which AI Evaluations?](https://oms-www.files.svdcdn.com/production/downloads/reports/Who%20should%20develop%20which%20AI%20evaluations.pdf?dm=1737016728)\n* [Why We Need to Know More: Exploring the State of AI Incident Documentation Practices](https://dl.acm.org/doi/fullHtml/10.1145/3600211.3604700)\n* [Worldwide AI Ethics: A Review of 200 Guidelines and Recommendations for AI Governance](https://arxiv.org/pdf/2206.11922)\n* [You Created A Machine Learning Application Now Make Sure It's Secure](https://www.oreilly.com/ideas/you-created-a-machine-learning-application-now-make-sure-its-secure)\n* [YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf) | Center for Countering Digital Hate (CCDH)\n\n#### Infographics and Cheat Sheets\n\n* [Foundation Model Development Cheatsheet](https://fmcheatsheet.org/)\n* Future of Privacy Forum\n  * [EU AI Act: A Comprehensive Implementation & Compliance Timeline](https://fpf.org/resource/eu-ai-act-a-comprehensive-implementation-compliance-timeline/)\n  * [The Spectrum of Artificial Intelligence](https://fpf.org/wp-content/uploads/2021/01/FPF_AIEcosystem_illo_03.pdf)\n* [Generative AI framework and Generative AI value tree modelling diagram](https://media.licdn.com/dms/image/v2/D4D22AQEKqP2a6_rsCw/feedshare-shrink_1280/B4DZP0cUWFHUAo-/0/1734972885448?e=1738195200&v=beta&t=PMJq6Ti1lisMMkyhnWojcdDt_DAlmYtV6MUQbqWu4hc)\n* [Global Index for AI Safety: AGILE Index on Global AI Safety Readiness Feb 2025](https://agile-index.ai/Global-Index-For-AI-Safety-Report-EN.pdf)\n* IAPP\n  * [EU AI Act Cheat Sheet](https://iapp.org/media/pdf/resource_center/eu_ai_act_cheat_sheet.pdf)\n  * [EU AI Act Compliance Matrix](https://iapp.org/resources/article/eu-ai-act-compliance-matrix/)\n* [Machine Learning Attack_Cheat_Sheet](https://resources.oreilly.com/examples/0636920415947/-/blob/master/Attack_Cheat_Sheet.png)\n* [Navigating the EU AI Act: A Process Map for making AI Systems available](https://www.appliedai-institute.de/assets/files/EU_AI_Act_Compliance_Journey.pdf) | AppliedAI Institute\n* [Oliver Patel's Cheat Sheets](https://www.linkedin.com/in/oliver-patel/recent-activity/images/)\n\n#### AI Red-Teaming Resources\n\n##### Papers\n\n* [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://arxiv.org/pdf/2411.00640)\n* [Exploiting Novel GPT-4 APIs](https://arxiv.org/abs/2312.14302)\n* [GenAI Red Teaming Guide: A Practical Approach to Evaluating AI Vulnerabilities](https://genai.owasp.org/download/44859/?tmstv=1737593350) | OWASP Version 1.0, January 23, 2025\n* [Identifying and Eliminating CSAM in Generative ML Training Data and Models](https://purl.stanford.edu/kh752sm9123)\n* [Jailbreaking Black Box Large Language Models in Twenty Queries](https://arxiv.org/abs/2310.08419)\n* [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://arxiv.org/abs/2404.08144)\n  * [No, LLM Agents can not Autonomously Exploit One-day Vulnerabilities](https://struct.github.io/auto_agents_1_day.html)\n* [Red Teaming for GenAI Harms: Revealing the Risks and Rewards for Online Safety](https://www.ofcom.org.uk/siteassets/resources/documents/consultations/discussion-papers/red-teaming/red-teaming-for-gen-ai-harms.pdf?v=370762) | Ofcom, July 23, 2024\n* [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://arxiv.org/abs/2209.07858)\n* [Red Teaming of Advanced Information Assurance Concepts](https://ieeexplore.ieee.org/document/821513)\n\n##### Tools and Guidance\n\n* [@dotey on X/Twitter exploring GPT prompt security and prevention measures](https://x.com/dotey/status/1724623497438155031?s=20)\n* [0xeb / GPT-analyst](https://github.com/0xeb/gpt-analyst/) | ![](https://img.shields.io/github/stars/0xeb/gpt-analyst?style=social)\n* [0xk1h0 / ChatGPT \"DAN\" and other \"Jailbreaks\"](https://github.com/0xk1h0/ChatGPT_DAN) | ![](https://img.shields.io/github/stars/0xk1h0/ChatGPT_DAN?style=social)\n* [A Safe Harbor for AI Evaluation and Red Teaming](https://arxiv.org/pdf/2403.04893)\n* [ACL 2024 Tutorial: Vulnerabilities of Large Language Models to Adversarial Attacks](https://llm-vulnerability.github.io/)\n* [Azure's PyRIT](https://github.com/Azure/PyRIT) | ![](https://img.shields.io/github/stars/Azure/PyRIT?style=social)\n* [Berkeley Center for Long-Term Cybersecurity](https://cltc.berkeley.edu/publication/benchmark-early-and-red-team-often-a-framework-for-assessing-and-managing-dual-use-hazards-of-ai-foundation-models/)\n* [CDAO frameworks, guidance, and best practices for AI test & evaluation](https://gitlab.jatic.net/home/frameworks)\n* [ChatGPT_system_prompt](https://github.com/LouisShark/chatgpt_system_prompt) | ![](https://img.shields.io/github/stars/LouisShark/chatgpt_system_prompt?style=social)\n* [coolaj86 / Chat GPT \"DAN\" and other \"Jailbreaks\"](https://gist.github.com/coolaj86/6f4f7b30129b0251f61fa7baaa881516) | ![](https://img.shields.io/github/stars/coolaj86?style=social)\n* [CSET, What Does AI-Red Teaming Actually Mean?](https://cset.georgetown.edu/article/what-does-ai-red-teaming-actually-mean/)\n* [DAIR Prompt Engineering Guide](https://www.promptingguide.ai/)\n  * [DAIR Prompt Engineering Guide GitHub](https://github.com/dair-ai/Prompt-Engineering-Guide) | ![](https://img.shields.io/github/stars/dair-ai/Prompt-Engineering-Guide?style=social)\n* [Extracting Training Data from ChatGPT](https://not-just-memorization.github.io/extracting-training-data-from-chatgpt.html)\n* [Frontier Model Forum: What is Red Teaming?](https://www.frontiermodelforum.org/uploads/2023/10/FMF-AI-Red-Teaming.pdf)\n* [Generative AI Red Teaming Challenge: Transparency Report 2024](https://drive.google.com/file/d/1JqpbIP6DNomkb32umLoiEPombK2-0Rc-/view)\n* [HackerOne, An Emerging Playbook for AI Red Teaming with HackerOne](https://www.hackerone.com/thought-leadership/ai-safety-red-teaming)\n* [Humane Intelligence, SeedAI, and DEFCON AI Village, Generative AI Red Teaming Challenge: Transparency Report 2024](https://drive.google.com/file/d/1JqpbIP6DNomkb32umLoiEPombK2-0Rc-/view)\n* [In-The-Wild Jailbreak Prompts on LLMs](https://github.com/verazuo/jailbreak_llms) | ![](https://img.shields.io/github/stars/verazuo/jailbreak_llms?style=social)\n* [Learn Prompting, Prompt Hacking](https://learnprompting.org/docs/category/-prompt-hacking)\n  * [MiesnerJacob / learn-prompting, Prompt Hacking](https://github.com/MiesnerJacob/learn-prompting/blob/main/08.%F0%9F%94%93%20Prompt%20Hacking.ipynb) | ![](https://img.shields.io/github/stars/MiesnerJacob/learn-prompting?style=social)\n* [leeky: Leakage/contamination testing for black box language models](https://github.com/mjbommar/leeky) | ![](https://img.shields.io/github/stars/mjbommar/leeky?style=social)\n* [LLM Security & Privacy](https://github.com/chawins/llm-sp) | ![](https://img.shields.io/github/stars/chawins/llm-sp?style=social)\n* [Membership Inference Attacks and Defenses on Machine Learning Models Literature](https://github.com/HongshengHu/membership-inference-machine-learning-literature) | ![](https://img.shields.io/github/stars/HongshengHu/membership-inference-machine-learning-literature?style=social)\n* [Lakera AI's Gandalf](https://gandalf.lakera.ai/)\n* [leondz / garak](https://github.com/leondz/garak) | ![](https://img.shields.io/github/stars/leondz/garak?style=social)\n* [Microsoft AI Red Team building future of safer AI](https://www.microsoft.com/en-us/security/blog/2023/08/07/microsoft-ai-red-team-building-future-of-safer-ai/)\n* [OpenAI Red Teaming Network](https://openai.com/blog/red-teaming-network)\n* [r/ChatGPTJailbreak](https://www.reddit.com/r/ChatGPTJailbreak/)\n  * [developer mode fixed](https://www.reddit.com/r/ChatGPTJailbreak/comments/144905t/developer_mode_fixed/)\n* [Y Combinator, ChatGPT Grandma Exploit](https://news.ycombinator.com/item?id=35630801)\n\n#### Generative AI Explainability\n\n* [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](http://sameersingh.org/files/papers/allennlp-interpret-demo-emnlp19.pdf)\n* Anthropic\n  * [Chain-of-thought Faithfulness](https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-cot)\n  * [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html)\n  * [Towards Monosemanticity: Decomposing Language Models With Dictionary Learning](https://transformer-circuits.pub/2023/monosemantic-features/index.html)\n  * [Tracing the thoughts of a large language model](https://www.anthropic.com/research/tracing-thoughts-language-model)\n* [Attention Is All You Need](https://arxiv.org/pdf/1706.03762)\n* [Backpack Language Models](https://arxiv.org/pdf/2305.16765)\n* Jay Alammar\n  * [Finding the Words to Say: Hidden State Visualizations for Language Models](https://jalammar.github.io/hidden-states/)\n  * [Interfaces for Explaining Transformer Language Models](https://jalammar.github.io/explaining-transformers/)\n* [Neuronpedia](https://www.neuronpedia.org/)\n* [Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph](https://openreview.net/forum?id=dWYRjT501w)[![Static Badge](https://img.shields.io/badge/pyPI-latent--explorer-red)](https://github.com/Ipazia-AI/latent-explorer)\n\n#### University Policies and Guidance\n\n* [Brown University, Classroom Policies for AI Generative Tools (PDF)](https://sheridan.brown.edu/sites/default/files/2023-09/Classroom-Policies-for-AI-Generative-Tools.pdf)\n* [Carnegie Mellon University, Generative Artificial Intelligence (AI) (Computing Services)](https://www.cmu.edu/computing/services/ai/)\n* [Carnegie Mellon University, Use AI Safely at CMU](https://www.cmu.edu/computing/services/ai/meet-ai/secure-ai.html)\n* [Columbia Business School, Generative AI Policy](https://students.business.columbia.edu/office-of-student-affairs/academic-advising-and-student-success/academic-integrity/generative-ai-policy)\n* [Columbia University, Considerations for AI Tools in the Classroom](https://ctl.columbia.edu/resources-and-technology/resources/ai-tools/)\n* [Columbia University, Generative AI Policy](https://provost.columbia.edu/content/office-senior-vice-provost/ai-policy)\n* [Cornell University, GenAI in Teaching and Learning (Academic Innovation)](https://academicinnovation.cornell.edu/gen-ai/)\n* [Cornell University, Generative Artificial Intelligence (Center for Teaching Innovation)](https://teaching.cornell.edu/generative-artificial-intelligence)\n* [Duke University, Artificial Intelligence Policies: Guidelines and Considerations](https://lile.duke.edu/ai-and-teaching-at-duke-2/artificial-intelligence-policies-in-syllabi-guidelines-and-considerations/)\n* [Duke University, Generative AI and Teaching at Duke](https://lile.duke.edu/ai-and-teaching-at-duke-2/)\n* [Durham University, Common Awards Policy and Guidance on Generative AI (PDF)](https://www.durham.ac.uk/media/durham-university/departments-/common-awards/documents/AI-Policy-and-Guidance.pdf)\n* [George Washington University, Faculty Resources: Generative AI](https://guides.himmelfarb.gwu.edu/faculty/generative-AI)\n* [George Washington University, Guidelines for Using Generative Artificial Intelligence at the George Washington University April 2023](https://provost.gwu.edu/sites/g/files/zaxdzs5926/files/2023-04/generative-artificial-intelligence-guidelines-april-2023.pdf)\n* [George Washington University, Guidelines for Using Generative Artificial Intelligence in Connection with Academic Work](https://provost.gwu.edu/guidelines-using-generative-artificial-intelligence-connection-academic-work-0)\n* [Georgetown University, Artificial Intelligence and Homework Support Policies](https://cndls.georgetown.edu/resources/syllabus-policies/ai-and-homework-support/)\n* [Georgetown University, Artificial Intelligence Generative Resources](https://guides.library.georgetown.edu/ai)\n* [Georgetown University, Teaching with AI](https://cndls.georgetown.edu/resources/ai/)\n* [Georgia Institute of Technology, Generative AI Guidance (OIT)](https://oit.gatech.edu/ai/guidance)\n* [Harvard Business School, 2.1.2 Using ChatGPT & Artificial Intelligence Tools](https://www.hbs.edu/mba/handbook/standards-of-conduct/academic/Pages/chatgpt-and-ai.aspx)\n* [Harvard Graduate School of Education, HGSE AI Policy](https://registrar.gse.harvard.edu/AI-policy)\n* [Harvard University, Guidelines for Using ChatGPT and other Generative AI tools at Harvard](https://provost.harvard.edu/guidelines-using-chatgpt-and-other-generative-ai-tools-harvard)\n* [Johns Hopkins University, Generative AI Tool Guidance: Syllabus Statements (Teaching @ JHU)](https://teaching.jhu.edu/university-teaching-policies/generative-ai/syllabus-statements/)\n* [Massachusetts Institute of Technology, Generative AI & Your Course](https://tll.mit.edu/teaching-resources/course-design/gen-ai-your-course/)\n* [Massachusetts Institute of Technology, Guidance for use of Generative AI tools](https://ist.mit.edu/ai-guidance)\n* [McMaster University, Provisional Guidelines on the Use of Generative AI in Teaching and Learning](https://provost.mcmaster.ca/generative-artificial-intelligence-2/task-force-on-generative-ai-in-teaching-and-learning/provisional-guidelines-on-the-use-of-generative-ai-in-teaching-and-learning/)\n* [New York University, Student Learning with Generative AI](https://www.nyu.edu/faculty/teaching-and-learning-resources/Student-Learning-with-Generative-AI.html)\n* [New York University, Teaching with Generative Tools](https://www.nyu.edu/faculty/teaching-and-learning-resources/teaching-with-generative-tools.html)\n* [Northwestern University, Guidance on the Use of Generative AI (Northwestern IT)](https://www.it.northwestern.edu/about/policies/guidance-on-the-use-of-generative-ai.html)\n* [Northwestern University, Use of Generative Artificial Intelligence in Courses](https://ai.northwestern.edu/education/use-of-generative-artificial-intelligence-in-courses.html)\n* [Oxford Brookes University, GenAI University wide policy and practice](https://www.brookes.ac.uk/staff/working-at-brookes/learning-and-career-development/academic-enhancement-and-development/teaching-and-learning/genai-university-wide-policy-and-practice)\n* [Pennsylvania State University, AI Hub Guidelines](https://ai.psu.edu/guidelines/)\n* [Princeton University Library, Disclosing the Use of AI](https://libguides.princeton.edu/generativeAI/disclosure)\n* [Princeton University, Generative AI Tools Use Policy](https://oit.princeton.edu/policies/generative-ai-tools-use-policy)\n* [Princeton University, Generative AI](https://oit.princeton.edu/generative-ai)\n* [Rutgers University, Guidance on the use of AI at Rutgers](https://it.rutgers.edu/ai/guidance-on-the-use-of-ai-at-rutgers/)\n* [Stanford Graduate School of Business, Course Policies on Generative AI Use](https://tlhub.stanford.edu/docs/course-policies-on-generative-ai-use/)\n* [Stanford University, Artificial Intelligence Teaching Guide](https://teachingcommons.stanford.edu/teaching-guides/artificial-intelligence-teaching-guide)\n* [Stanford University, Creating your course policy on AI](https://teachingcommons.stanford.edu/teaching-guides/artificial-intelligence-teaching-guide/creating-your-course-policy-ai)\n* [Stanford University, Generative AI Policy Guidance](https://communitystandards.stanford.edu/generative-ai-policy-guidance)\n* [Stanford University, Responsible AI at Stanford](https://uit.stanford.edu/security/responsibleai)\n* [The Open University, Position Statement and Guidance: Generative AI and Doctoral Education](https://university.open.ac.uk/students/research/ou/services/position-statement-and-guidance-generative-ai-and-doctoral-education)\n* [Tufts University CELT, Developing Syllabus Statements for AI](https://provost.tufts.edu/celt/online-resources/artificial-intelligence/ai-syllabus-statements/)\n* [Tufts University, Guidelines for Use of Generative AI Tools](https://it.tufts.edu/guidelines-use-generative-ai-tools)\n* [University of Alberta, Report of the Provost’s Task Force on Artificial Intelligence and the Learning Environment (PDF)](https://www.ualberta.ca/en/provost/media-library/policies-and-procedures/report-of-the-provosts-task-force-on-artificial-intelligence-and-the-learning-environment.pdf)\n* [University of Birmingham, Generative Artificial Intelligence (AI) framework for teaching/learning/assessment](https://www.birmingham.ac.uk/libraries/education-excellence/gai)\n* [University of British Columbia, Resources (Generative AI)](https://genai.ubc.ca/resources/)\n* [University of British Columbia, Teaching & Learning Guidelines (Generative AI)](https://genai.ubc.ca/guidance/teaching-learning-guidelines/)\n* [University of California, AI Governance and Transparency](https://ai.universityofcalifornia.edu/governance-transparency/)\n* [University of California, Applicable Law and UC Policy](https://ai.universityofcalifornia.edu/governance-transparency/applicable-law-and-policy.html)\n* [University of California, Berkeley, AI at UC Berkeley](https://ai.berkeley.edu/home)\n* [University of California, Irvine, Generative AI for Teaching and Learning](https://dtei.uci.edu/generative-ai/)\n* [University of California, Legal Alert: Artificial Intelligence Tools](https://www.ucop.edu/ethics-compliance-audit-services/_files/compliance/ai/ai-alert.pdf)\n* [University of California, Los Angeles, Generative AI](https://genai.ucla.edu/)\n* [University of California, Los Angeles, Guiding Principles for Responsible Use](https://genai.ucla.edu/guiding-principles-responsible-use)\n* [University of California, Los Angeles, Teaching Guidance for ChatGPT and Related AI Developments](https://senate.ucla.edu/news/teaching-guidance-chatgpt-and-related-ai-developments)\n* [University of Cambridge (Blended Learning Service), Generative AI and Assessment](https://blendedlearning.cam.ac.uk/artificial-intelligence-and-education/generative-ai-and-assessment)\n* [University of Cambridge (Blended Learning Service), Using Generative AI](https://blendedlearning.cam.ac.uk/artificial-intelligence-and-education/using-generative-ai)\n* [University of Chicago, Generative AI Guidance](https://genai.uchicago.edu/en/about/generative-ai-guidance)\n* [University of Chicago, Guidance for Syllabus Statements on the Use of AI Tools](https://teaching.uchicago.edu/sites/default/files/2023-09/CCTL_AI%20Syllabus%20Statements.pdf)\n* [University of East Anglia, Generative AI Policy for Teaching and Learning (PDF)](https://assets.uea.ac.uk/f/185167/x/5558a0f812/generative-ai-policy-for-teaching-and-learning.pdf)\n* [University of Edinburgh, Generative AI Guidance for Students (PDF)](https://registryservices.ed.ac.uk/sites/default/files/2024-10/Generative%20AI%20Guidance%20for%20Students%20October%202024.pdf)\n* [University of Florida (CITT), Generative AI and Teaching](https://citt.it.ufl.edu/services/learning-innovation-and-technology/artificial-intelligence/gen-ai-and-teaching/)\n* [University of Florida, Guidance for Instructors (Teaching with AI)](https://ai.ufl.edu/teaching-with-ai/expanding-the-ai-curriculum/guidance-for-instructors/)\n* [University of Illinois Chicago, Generative AI Resources and Guidance for Faculty](https://provost.uic.edu/news-stories/generative-ai-resources-and-guidance-for-faculty/)\n* [University of Illinois Urbana-Champaign, Generative AI Guidance (Graduate College)](https://grad.illinois.edu/academics/thesis-dissertation/writing-your-thesis/ai-guidance)\n* [University of Illinois Urbana-Champaign, Teaching Implications of Generative Artificial Intelligence (CITL)](https://citl.illinois.edu/citl-101/teaching-learning/resources/teaching-implications-of-generative-artificial-intelligence)\n* [University of Kent, AI Use Guidelines (Using Generative AI in your studies)](https://student.kent.ac.uk/studies/using-generative-ai-in-your-studies/ai-use-guidelines)\n* [University of Leeds, AI and assessments (Generative AI)](https://generative-ai.leeds.ac.uk/ai-and-assessments/)\n* [University of Leeds, Generative AI guidance for taught students (PDF)](https://generative-ai.leeds.ac.uk/wp-content/uploads/sites/134/2023/12/UoL-GenAI-guidance-for-taught-students.pdf)\n* [University of Leicester, Policy on Generative AI in Learning, Teaching and Assessment (PDF)](https://le.ac.uk/-/media/uol/docs/policies/quality/ai-policy.pdf)\n* [University of Liverpool, AI at Liverpool: Policies and Guidance](https://www.liverpool.ac.uk/about/the-university/reports-policies-and-governance/ai-at-liverpool/policies-and-guidance/)\n* [University of Maryland, Guidelines for Use (AI @ UMD)](https://ai.umd.edu/resources-guidelines/guidelines-for-use)\n* [University of Michigan (CRLT), Academic Integrity, GenAI](https://crlt.umich.edu/academic-integrity-genai)\n* [University of Michigan, Generative AI Academic Integrity Resources](https://academictechnology.umich.edu/instructional-resources/generative-ai/academic-integrity-resources)\n* [University of Michigan, Guidance for Faculty/Instructors (GenAI)](https://genai.umich.edu/resources/faculty)\n* [University of Minnesota, Teaching with Generative AI](https://teachingsupport.umn.edu/teaching-generative-ai)\n* [University of Northern British Columbia, Guidance on using generative AI](https://www.unbc.ca/provost/guidance-acceptability-using-generative-ai-coursework)\n* [University of Notre Dame, AI Recommendations for Instructors](https://honorcode.nd.edu/ai-recommendations-for-instructors/)\n* [University of Notre Dame, AI@ND Policies and Guidelines](https://ai.nd.edu/policies-and-guidelines/)\n* [University of Notre Dame, Generative AI Policy for Students](https://honorcode.nd.edu/generative-ai-policy-for-students-august-2023/)\n* [University of Pennsylvania, Penn AI Guidance and Policies (CETLI)](https://cetli.upenn.edu/resources/generative-ai/penn-ai-guidance-and-policies/)\n* [University of Pennsylvania, Statement on Guidance for the Penn Community on Use of Generative Artificial Intelligence (ISC)](https://isc.upenn.edu/security/AI-guidance)\n* [University of Pittsburgh, Teaching with Generative AI](https://teaching.pitt.edu/resources/teaching-with-generative-ai/)\n* [University of Portsmouth, AI Guidance for teaching staff](https://www.port.ac.uk/policy/ai-guidance-for-teaching-staff)\n* [University of South Carolina, Report of the 2024–2025 Provost’s Task Force on the Use of AI Tools in Teaching and Learning](https://sc.edu/about/offices_and_divisions/cte/teaching_resources/docs/ai_task_force_report.pdf)\n* [University of Southampton, Using Generative AI During Your Studies](https://www.southampton.ac.uk/about/governance/regulations-policies/policies/using-gen-ai-during-your-studies)\n* [University of Southern California, Using Generative AI in Research](https://libguides.usc.edu/generative-AI/home)\n* [University of Texas at Austin, Generative AI in Teaching and Learning: Policies](https://ctl.utexas.edu/generative-ai-teaching-and-learning-policies)\n* [University of Texas at Austin, Guidance for Using Artificial Intelligence (Enterprise Technology)](https://tech.utexas.edu/governance/guidance-for-ai)\n* [University of Toronto (School of Graduate Studies), Guidance on the Use of Generative Artificial Intelligence](https://www.sgs.utoronto.ca/about/guidance-on-the-use-of-generative-artificial-intelligence/)\n* [University of Virginia, Guidance for Faculty & Students (Generative AI in Teaching and Learning)](https://genai.provost.virginia.edu/guidance-for-faculty-students)\n* [University of Washington, AI+Teaching, Sample syllabus statements regarding student use of artificial intelligence](https://teaching.washington.edu/course-design/ai/sample-ai-syllabus-statements/)\n* [University of Washington, AI+Teaching](https://teaching.washington.edu/course-design/ai/)\n* [University of Wisconsin–Madison, Statement on Use of Generative AI](https://it.wisc.edu/generative-ai-services-uw-madison/statement-on-use-of-generative-ai/)\n* [Vanderbilt University, Sample Syllabi Statements for Generative AI and ChatGPT Usage (PDF)](https://cdn.vanderbilt.edu/vu-sub/wp-content/uploads/sites/59/2023/09/07192929/Sample-Syllabi-Statements-for-Generative-AI-and-ChatGPT-Usage.pdf)\n* [Virginia Tech, Considering Generative AI at Virginia Tech](https://tlos.vt.edu/resources/generative-ai.html)\n* [Yale University, AI at Yale](https://ai.yale.edu/)\n* [Yale University, AI Guidance for Teachers](https://poorvucenter.yale.edu/AIguidance)\n* [Yale University, Guidelines for the Use of Generative AI Tools](https://provost.yale.edu/news/guidelines-use-generative-ai-tools)\n\n\n### Official Policy, Frameworks, and Guidance\n\nThis section serves as a repository for policy documents, regulations, guidelines, and recommendations that govern the ethical and responsible use of artificial intelligence and machine learning technologies. From international legal frameworks to specific national laws, the resources cover a broad spectrum of topics such as fairness, privacy, ethics, and governance.\n\n#### Australia\n\n* Department of Industry, Science and Resources\n  * [AI Governance: Leadership insights and the Voluntary AI Safety Standard in practice](https://www.industry.gov.au/news/ai-governance-leadership-insights-and-voluntary-ai-safety-standard-practice) | Department of Industry, Science and Resources\n  * [AI Plan for the Australian Public Service](https://www.digital.gov.au/sites/default/files/documents/2025-11/APS%20AI%20Plan%202025.pdf) | 2025\n  * [Artificial Intelligence Model Clauses](https://www.buyict.gov.au/sys_attachment.do?sys_id=e535e2ca935caa10438b39cdfaba103d) | Digital Transformation Agency, Version 2.0, March 2025\n  * [Australian AI Security Framework Index](https://github.com/Benjamin-KY/Australian-AI-Security) | ![](https://img.shields.io/github/stars/Benjamin-KY/Australian-AI-Security?style=social)\n  * [Australia’s AI Ethics Principles](https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles)\n  * [Guidance for AI Adoption](https://www.industry.gov.au/publications/guidance-for-ai-adoption) | National Artificial Intelligence Centre, October 21, 2025\n    * [Guidance for AI Adoption: Foundations v1.0](https://www.industry.gov.au/sites/default/files/2025-10/guidance-for-ai-adoption-foundations.pdf) | National Artificial Intelligence Centre, October 2025\n    * [Guidance for AI Adoption: Implementation practices v1.0](https://www.industry.gov.au/sites/default/files/2025-10/guidance-for-ai-adoption-implementation-practices.pdf) | National Artificial Intelligence Centre, October 2025 \n* [Guide for Australian Business: Understanding 42001](https://cdn.prod.website-files.com/6420f704f2602a2ee7f79d26/662aefb77b3077382ff25eef_understanding%2042001%20ai%20management%20system%20standard%20whitepaper.pdf) | AS ISO/IEC 42001:2023, Information Technology – Artificial Intelligence – Management System | Standards Australia and National Artificial Intelligence Centre\n  * [Introducing mandatory guardrails for AI in high-risk settings: proposals paper](https://consult.industry.gov.au/ai-mandatory-guardrails)\n  * [The AI Impact Navigator](https://www.industry.gov.au/publications/ai-impact-navigator) | October 21, 2024\n  * [Voluntary AI Safety Standard](https://www.industry.gov.au/sites/default/files/2024-09/voluntary-ai-safety-standard.pdf) |  August 2024\n* Digital Transformation Agency\n  * [Evaluation of the whole-of-government trial of Microsoft 365 Copilot: Summary of evaluation findings](https://www.digital.gov.au/sites/default/files/documents/2024-10/Copilot%20Microsoft%20365%20summary%20of%20evaluation%20findings.pdf) | October 23, 2024\n  * [Policy for the responsible use of AI in government](https://www.digital.gov.au/sites/default/files/documents/2024-08/Policy%20for%20the%20responsible%20use%20of%20AI%20in%20government%20v1.1.pdf) | September 2024, Version 1.1\n* Office of the Australian Information Commissioner\n  * [Guidance on privacy and developing and training generative AI models](https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-developing-and-training-generative-ai-models)\n  * [Guidance on privacy and the use of commercially available AI products](https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products)\n* [National framework for the assurance of artificial intelligence in government](https://www.finance.gov.au/sites/default/files/2024-06/National-framework-for-the-assurance-of-AI-in-government.pdf)\n* [Technical standard for government’s use of artificial intelligence](https://www.digital.gov.au/policy/ai/AI-technical-standard) | Digital Transformation Agency (DTA)\n* [Testing the Reliability, Validity, and Equity of Terrorism Risk Assessment Instruments](https://www.homeaffairs.gov.au/foi/files/2023/fa-230400097-document-released-part-1.PDF)\n* [Understanding Responsibilities in AI Practices](https://www.digital.nsw.gov.au/policy/artificial-intelligence/nsw-artificial-intelligence-assessment-framework/responsibilities) | NSW Digital Strategy\n\n#### Brazil\n\n* [Autoridade Nacional de Proteção de Dados, Technology Radar – short version in English, no. 1: Generative Artificial Intelligence](https://www.gov.br/anpd/pt-br/documentos-e-publicacoes/documentos-de-publicacoes/radar-tecnologico-inteligencia-artificial-generativa-versao-em-lingua-inglesa.pdf) | (ANPD, Brazilian Data Protection Authority)\n\n#### Canada\n\n* [A Regulatory Framework for AI: Recommendations for PIPEDA Reform](https://www.priv.gc.ca/en/about-the-opc/what-we-do/consultations/completed-consultations/consultation-ai/reg-fw_202011/)\n* [An Act to enact the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act and the Artificial Intelligence and Data Act and to make consequential and related amendments to other Acts](https://www.parl.ca/legisinfo/en/bill/44-1/c-27)\n* [AI in Canada](https://oecd.ai/en/dashboards/countries/Canada) | OECD AI policies in Canada\n* [Algorithmic Impact Assessment tool](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html)\n* [Artificial Intelligence and Data Act](https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act)\n* [The Artificial Intelligence and Data Act Companion document](https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document) | (AIDA)\n* [Directive on Automated Decision Making](https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592) | (Canada)\n* [E-23 – Model Risk Management](https://www.osfi-bsif.gc.ca/en/guidance/guidance-library/draft-guideline-e-23-model-risk-management) | (Draft Guideline)\n* [Guideline E-23 – Model Risk Management 2027](https://www.osfi-bsif.gc.ca/en/guidance/guidance-library/guideline-e-23-model-risk-management-2027) | Office of the Superintendent of Financial Institutions\n* [Responsible use of artificial intelligence in government](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai.html) | Government of Canada\n* [Transparency for machine learning-enabled medical devices: Guiding principles](https://www.canada.ca/en/health-canada/services/drugs-health-products/medical-devices/transparency-machine-learning-guiding-principles.html) | Health Canada\n\n#### China\n\n* [人工智能全球治理行动计划](https://www.gov.cn/yaowen/liebiao/202507/content_7033929.htm) | Action Plan on Global Governance of Artificial Intelligence, July 26, 2025\n\n#### Colombia\n\n* [Presidency of the Republic of Colombia, Marco Ético para la Inteligencia Artificial en Colombia](https://minciencias.gov.co/sites/default/files/marco-etico-ia-colombia-2021.pdf) | Ethical Framework for Artificial Intelligence in Colombia, November 2021\n\n#### Costa Rica\n\n* [Ministerio de Ciencia, Innovación, Tecnología y Telecomunicaciones](https://cambioclimatico.go.cr/wp-content/uploads/2023/06/Plan-Nacional-Ciencia-Tecnologia-Innovacion-2022-2027.pdf) | MICITT, Plan Nacional de Ciencia, Tecnología e Innovación 2022–2027\n\n#### Croatia\n\n* [Etički kodeks za pripremu i provedbu projekata financiranih projektom Digitalne, inovativne i zelene tehnologije, DIGIT PROJEKT](https://mzom.gov.hr/UserDocsImages/dokumenti/Znanost/Projekt-digit/Eticki-kodeks-verzija-2-Projekt-DIGIT-10-3-2025-FINAL.pdf) | Ministry of Science, Education and Youth (MZOM), Ethical Code for the DIGIT Project, March 2025\n* [Digital Croatia Strategy for the period until 2032](https://mpudt.gov.hr/UserDocsImages/RDD/SDURDD-dokumenti/Strategija_Digitalne_Hrvatske_final_v1_EN.pdf)\n* [Nacionalni program zaštite potrošača za razdoblje do 2028. godine](https://vlada.gov.hr/UserDocsImages//2016/Sjednice/2025/Kolovoz/112_sjednica_VRH//112%20-%205a%20Program.pdf) | Ministry of Economy and Sustainable Development (MINGOR), National Consumer Protection Programme to 2028, July 2025\n* [Pametna sigurnost: Praktična primjena umjetne inteligencije i nosivih senzora u građevinarstvu](https://uznr.mrms.hr/wp-content/uploads/2025/04/09-Pametna-sigurnost.pdf) | Ministry of Labour, Pension System, Family and Social Policy (MRMS), \"Smart Safety\" campaign presentation on AI and wearable sensors in construction, April 2025\n* [Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence Volume 1 Croatia](https://www.oecd.org/en/publications/progress-in-implementing-the-european-union-coordinated-plan-on-artificial-intelligence-volume-1_6d530a88-en/croatia_38d8145c-en.html) | OECD\n  * [PDF here](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/10/progress-in-implementing-the-european-union-coordinated-plan-on-artificial-intelligence-volume-1-country-notes_b0385317/croatia_5665ea22/38d8145c-en.pdf)\n\n#### Denmark\n\n* [Danish strategies for artificial intelligence](https://en.digst.dk/digital-governance/new-technologies/danish-strategies-for-artificial-intelligence/) | Agency for Digital Government\n* [National Strategy for Artificial Intelligence](https://en.digst.dk/media/lz0fxbt4/305755_gb_version_final-a.pdf) | Ministry of Finance and Ministry of Industry, Business and Financial Affairs, March 2019\n* [National Strategy for Digitalisation: Together in the digital development](https://en.digst.dk/media/mndfou2j/national-strategy-for-digitalisation-together-in-the-digital-development.pdf) | Ministry of Finance, May 2022\n* [Progress in Implementing the EU Coordinated Plan on Artificial Intelligence, Denmark](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/10/progress-in-implementing-the-european-union-coordinated-plan-on-artificial-intelligence-volume-1-country-notes_b0385317/denmark_86fcfcf7/89c393fa-en.pdf) | OECD, 2025\n* [Strategic Approach to Artificial Intelligence: A more robust foundation for the responsible development and use of AI in Denmark](https://www.english.digmin.dk/Media/638719220318136690/Stategic%20Approach%20to%20Artificial%20Intelligence.pdf) | The Ministry of Digital Affairs, December 2024\n\n#### Finland\n\n* [AI Watch. European landscape on the use of Artificial Intelligence by the Public Sector](https://ai-watch.ec.europa.eu/publications/ai-watch-european-landscape-use-artificial-intelligence-public-sector_en) | European Commission, Joint Research Centre, June 1, 2022\n  * [PDF](https://ai-watch.ec.europa.eu/document/download/18fc56db-42f2-4cbd-990e-a9a0ccfd523f_en)\n* [Artificial Intelligence 4.0 First interim report: from launch to implementation stage](https://julkaisut.valtioneuvosto.fi/server/api/core/bitstreams/5b864f19-e2e8-47a1-b45a-f5c41d530ece/content) | Publications of the Ministry of Economic Affairs and Employment Companies 2021:53\n* [Finland AI Strategy Report](https://ai-watch.ec.europa.eu/countries/finland/finland-ai-strategy-report_en) | European Commission\n* [Finland's Age of Artificial Intelligence: Turning Finland into a leading country in the application of artificial intelligence. Objective and recommendations for measures](https://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/160391/TEMrap_47_2017_verkkojulkaisu.pdf) | Ministry of Economic Affairs and Employment\n* [Progress in Implementing the EU Coordinated Plan on Artificial Intelligence, Finland](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/10/progress-in-implementing-the-european-union-coordinated-plan-on-artificial-intelligence-volume-1-country-notes_b0385317/finland_e37ddb49/adc99184-en.pdf) | OECD, 2025\n\n#### France\n* [Adoption de l’IA — Fiches pratiques à destination des commerçants](https://www.entreprises.gouv.fr/files/files/Publications/2025/Guide/202507-guide-adoption-ia-commercants.pdf) | Conseil national du commerce / Direction générale des entreprises (Ministère de l’Économie), July 2025\n* [Bac à sable « IA et services publics »: Les recommandations de la CNIL aux lauréats](https://www.cnil.fr/sites/cnil/files/2025-04/bac_a_sable_recommandations.pdf) | CNIL, March 2025\n* [Challenges and opportunities of artificial intelligence in the fight against information manipulation](https://www.sgdsn.gouv.fr/files/files/Publications/20250207_NP_SGDSN_VIGINUM_Rapport%20menace%20informationnelle%20IA_EN_0.pdf) | VIGINUM, February 7, 2025\n* [Consultation publique, Fiches pratiques IA sur la mobilisation de l’intérêt legitime pour le développement de systèmes d’intelligence artificielle: Synthèse des contributions](https://www.cnil.fr/sites/default/files/2025-06/synthese_des_contributions_fiches_ia_interet_legitime.pdf) | CNIL, June 19, 2025\n* [Développement des systèmes d’IA : que faut-il vérifier ?](https://www.cnil.fr/sites/default/files/2025-07/ia_liste_de_verification.pdf) | CNIL, July 2025\n* [Fiche pratique pour l’achat responsable de solutions d’intelligence artificielle IA](https://ecoresponsable.numerique.gouv.fr/docs/2025/MINUMECO_fiche_IA_frugale_VF.pdf) | Numérique écoresponsable — Ministère de l’Économie, 2025\n* [France 2030 — Stratégie nationale pour l’intelligence artificielle, 2e phase: Conquérir les talents et transformer notre potentiel scientifique en succès économiques](https://presse.economie.gouv.fr/wp-content/uploads/2021/11/8bcf2b43571df79a59055eab0cc5047e.pdf) | Ministry of Economy, November 8, 2021\n* [Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier](https://acpr.banque-france.fr/sites/default/files/medias/documents/20200612_gouvernance_evaluation_ia.pdf)\n* [Guide d’implémentation de l’éthique dans les systèmes d’intelligence artificielle en santé](https://esante.gouv.fr/sites/default/files/media_entity/documents/guide-ia_vf.pdf) | Ministère du Travail, de la Santé, des Solidarités et des Familles — Délégation au numérique en santé, July 2025\n* [Guide de la génération augmentée par récupération RAG](https://www.entreprises.gouv.fr/files/files/Publications/2024/Guides/20241127-bro-guide-ragv4-interactif.pdf) | Direction générale des entreprises (Ministère de l’Économie, des Finances et de l’Industrie), November 2024\n* [IA : Notre ambition pour la France](https://www.economie.gouv.fr/files/files/directions_services/cge/commission-IA.pdf) | Ministry of Economy, Commission de l’intelligence artificielle\n* [Les risques associés à l’usage de l’intelligence artificielle dans le monde professionnel](https://www.dgsi.interieur.gouv.fr/dgsi-a-vos-cotes/contre-espionnage/conseils-aux-entreprises-flash-ingerence/risques-associes-a-lusage-de-lintelligence-artificielle-dans-monde-professionnel) | Direction générale de la Sécurité intérieure (Ministère de l’Intérieur), 2025\n* [Security Recommendations for a Generative AI System](https://messervices.cyber.gouv.fr/documents-guides/security_recommandations_for_a_generative_ai_system.pdf) | ANSSI Guidelines, ANSSI-PA-102, 29/04/2024\n\n#### Germany\n\n* Bundesamt für Sicherheit in der Informationstechnik\n  * [Generative AI Models - Opportunities and Risks for Industry and Authorities](https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/KI/Generative_AI_Models.html)\n  * [German-French recommendations for the use of AI programming assistants](https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/KI/ANSSI_BSI_AI_Coding_Assistants.html)\n* [Germany AI Strategy Report](https://ai-watch.ec.europa.eu/countries/germany/germany-ai-strategy-report_en)\n* [OECD-Bericht zu Künstlicher Intelligenz in Deutschland](https://www.ki-strategie-deutschland.de/files/downloads/OECD-Bericht_K%C3%BCnstlicher_Intelligenz_in_Deutschland.pdf)\n* [Recommendations of the Data Ethics Commission for the Federal Government's Strategy on Artificial Intelligence,](https://www.bmi.bund.de/SharedDocs/downloads/EN/themen/it-digital-policy/recommendations-data-ethics-commission.pdf?__blob=publicationFile&v=3) |  Daten Ethik Kommission, October 9, 2018\n\n#### Hong Kong\n\n* [Artificial Intelligence: Model Personal Data Protection Framework](https://www.pcpd.org.hk/english/resources_centre/publications/files/ai_protection_framework.pdf) | Office of the Privacy Commissioner for Personal Data, June 2024\n\n#### Iceland\n\n* Ministry of Higher Education, Industry, and Innovation\n  * [Aðgerðaáætlun um gervigreind 2024-2026](https://samradapi.island.is/api/Documents/1d4c7cba-fd9c-ef11-9bc7-005056bcce7e) | Action Plan for Artificial Intelligence 2024-2026 | November 2024\n  * [Efnahagsleg tækifæri gervigreindar á Íslandi](https://samradapi.island.is/api/Documents/1e4c7cba-fd9c-ef11-9bc7-005056bcce7e) | Economic Opportunities of Artificial Intelligence in Iceland, Statistics Iceland\n\n#### India\n\n* [AI Governance Framework for India 2025-26](https://www.aigl.blog/content/files/2025/09/AI-Governance-Framework-for-India.pdf) | National Cyber and AI Center\n* [Stakeholders consultation on \"Draft Standard for the Schema and Taxonomy of an AI Incident Database in Telecommunications and Critical Digital Infrastructure\"](https://www.tec.gov.in/pdf/consultations/TEC_57090.pdf) | Telecommunications Engineering Centre, May 27, 2025\n\n#### Ireland\n\n* [AI - Here for Good: A National Artificial Intelligence Strategy for Ireland](https://enterprise.gov.ie/en/publications/publication-files/national-ai-strategy.pdf)\n* [AI Standards & Assurance Roadmap: Action under 'AI - Here for Good,' the National Artificial Intelligence Strategy for Ireland](https://www.nsai.ie/images/uploads/general/NSAI_AI_report_digital_links.pdf) | National Standards Authority of Ireland, Top Team on Standards in AI, July 2023\n* [Artificial Intelligence: Friend or Foe? Summary and Public Policy Considerations](https://www.gov.ie/pdf/?file=https://assets.gov.ie/295620/f11c6c66-4012-49fa-bb7f-8f14130f6fa5.pdf) | Department of Finance and Department of Enterprise, Trade and Employment, June 2024\n* [Interim Guidelines for Use of AI in the Public Service](https://assets.gov.ie/280459/73ce75af-0015-46af-a9f6-b54f0a3c4fd0.pdf) | Department of Public Expenditure, NDP Delivery and Reform, February 2024\n* [Ireland's National AI Strategy: AI - Here for Good](https://enterprise.gov.ie/en/publications/publication-files/national-ai-strategy-refresh-2024.pdf) | Refresh 2024\n\n#### Italy\n\n* [Bozza di linee guida per l’adozione di IA nella pubblica amministrazione](https://www.agid.gov.it/sites/agid/files/2025-02/Linee_Guida_adozione_IA_nella_PA.pdf) | Agenzia per l’Italia Digitale (AgID), Draft (Bozza) – Version 1.0 dated 14 Feb 2025. Public consultation from 18 Feb to 20 March 2025.\n* [Linee Guida per l’Introduzione dell’Intelligenza Artificiale nelle Istituzioni Scolastiche](https://www.mim.gov.it/documents/20182/0/MIM_Linee%2Bguida%2BIA%2Bnella%2BScuola_09_08_2025-signed.pdf/b70fdc45-4b75-1f7e-73bf-eab12989b928?t=1756468797694) | Versione 1.0 – Anno 2025, Ministero dell’Istruzione e del Merito\n* [Piano Triennale per l’Informatica nella Pubblica Amministrazione](https://presidenza.governo.it/AmministrazioneTrasparente/DisposizioniGenerali/AttiGenerali/AttiAmministrativiGenerali/Aggiornamento_PianoTriennale_DTD.pdf) | Edizione 2024–2026, Aggiornamento 2026, Agenzia per l’Italia Digitale (AGID)\n* [Strategia Italiana per l’Intelligenza Artificiale 2024–2026](https://www.agid.gov.it/sites/agid/files/2024-07/Strategia_italiana_per_l_Intelligenza_artificiale_2024-2026.pdf) | Dipartimento per la Trasformazione Digitale and the Agenzia per l’Italia Digitale (AGID)\n\n#### Jamaica\n\n* [National Artificial Intelligence Policy Recommendations](https://opm.gov.jm/wp-content/uploads/2025/02/National-Artificial-Intelligence-Task-Force-Policy-Recommendations-Final-1.pdf) | National Artificial Intelligence Task Force, September 2024\n\n#### Japan\n\n* Japan AI Safety Institute\n  * [Guide to Evaluation Perspectives on AI Safety](https://aisi.go.jp/assets/pdf/ai_safety_eval_v1.01_en.pdf) | (Version 1.01), September 25, 2024\n  * [Guide to Red Teaming Methodology on AI Safety](https://aisi.go.jp/assets/pdf/ai_safety_RT_v1.00_en.pdf) | (Version 1.00), September 25, 2024\n \n#### Kenya\n\n* [Diplomat's Playbook on Artificial Intelligence—Shaping a Safe, Secure, Inclusive, and Trustworthy AI Future: Kenya's Strategic Leadership in AI Global Diplomacy](https://mfa.go.ke/sites/default/files/2025-01/DIPLOMATS%20AI%20PLAYBOOK%20FINAL.pdf) | Ministry of Foreign and Diaspora Affairs, State Department for Foreign Affairs, and the Office of the Special Envoy on Technology\n* [Kenya Artificial Intelligence Strategy 2025-2030](https://ict.go.ke/sites/default/files/2025-03/Kenya%20AI%20Strategy%202025%20-%202030.pdf) | March 2025\n\n#### Malaysia\n\n* [The National Guidelines on AI Governance & Ethics](https://mastic.mosti.gov.my/publication/the-national-guidelines-on-ai-governance-ethics/)\n\n#### Mexico\n\n* [Recomendaciones para el Tratamiento de Datos Personales Derivado del Uso de la Inteligencia Artificial](https://home.inai.org.mx/wp-content/documentos/DocumentosSectorPublico/RecomendacionesPDP-IA.pdf) | Instituto Nacional de Transparencia, Acceso a la Información y Protección de Datos Personales (INAI), June 2024\n\n#### Moldova\n\n* Ministry of Economic Development and Digitalization\n  * [Cartea Albă cu Privire la Inteligența Artificială și Guvernanța Datelor](https://drive.google.com/file/d/1MDEGQ3snOiYXeM5G1YZfV8yH6ZFWxVTJ/view) | 2024\n  * [White Book on Artificial Intelligence and Data Governance](https://drive.google.com/file/d/1d2VmubZJjwVjzxUT4gjJE7DXTinzdyfO/view?usp=sharing) | 2024\n\n#### Netherlands\n\n* [AI Act Guide](https://www.government.nl/binaries/government/documenten/publications/2025/09/04/ai-act-guide/ai-act-guide.pdf) | Ministry of Economic Affairs\n* [AI Impact Assessment: The tool for a responsible AI project](https://www.government.nl/binaries/government/documenten/publications/2023/03/02/ai-impact-assessment/2024-IWM-AI-Impact-assessment-2.0-EN.pdf) | Ministry of Infrastructure and Water Management\n* Autoriteit Persoonsgegevens\n  * [Call for input on prohibition on AI systems for emotion recognition in the areas of workplace or education institutions](https://www.autoriteitpersoonsgegevens.nl/en/documents/call-for-input-on-prohibition-on-ai-systems-for-emotion-recognition-in-the-areas-of-workplace-or-education-institutions) | October 31, 2024\n  * [AScraping bijna altijd illegal](https://www.autoriteitpersoonsgegevens.nl/actueel/ap-scraping-bijna-altijd-illegaal) | Dutch Data Protection Authority, \"Scraping is always illegal\"\n* [General principles for the use of Artificial Intelligence in the financial sector](https://www.dnb.nl/media/jkbip2jc/general-principles-for-the-use-of-artificial-intelligence-in-the-financial-sector.pdf)\n* [Pathways on capacity building for AI supervisory authorities: insights and recommendations from the 1st UNESCO expert roundtable on AI supervision](https://unesdoc.unesco.org/ark:/48223/pf0000396637) | UNESCO and Dutch Authority for Digital Infrastructure, 2025\n\n#### New Zealand\n\n* [Accredited Employer Work Visa: Use of Adept for Automated Processing of Migrant Gateway](https://www.mbie.govt.nz/dmsdocument/28176-accredited-employer-work-visa-use-of-adept-for-automated-processing-of-migrant-gateway) | Ministry of Business, Innovation & Employment, June 28, 2022\n* [Advanced AI evaluations at AISI: May update](https://www.aisi.gov.uk/work/advanced-ai-evaluations-may-update) | AI Safety Institute (AISI)\n* [Algorithm Assessment Report](https://www.data.govt.nz/assets/Uploads/Algorithm-Assessment-Report-Oct-2018.pdf) | Internal Affairs and Stats NZ, October 2018\n* [Algorithm Charter for Aotearoa New Zealand](https://data.govt.nz/assets/data-ethics/algorithm/Algorithm-Charter-2020_Final-English-1.pdf)\n* [Algorithm impact assessment user guide: Algorithm Charter for Aotearoa New Zealand](https://www.data.govt.nz/assets/data-ethics/algorithm/AIA-user-guide.pdf) | December 2023\n* [Artificial intelligence frameworks and regulation: An intelligence perspective](https://igis.govt.nz/assets/Uploads/FINAL-Part-1_-Global-AI-frameworks-and-regulation.pdf) | Inspector-General of Intelligence and Security, August 2024\n* [Automated decision-making in MSD: Proposed legislative and policy framework](https://www.msd.govt.nz/documents/about-msd-and-our-work/publications-resources/official-information-responses/2022/july/07072022-requesting-the-document-automated-decision-making-in-msd-proposed-legislative-and-policy-framework-memo-.pdf) | Ministry of Social Development, May 5, 2021\n* [Automated Decision Making Standard](https://www.msd.govt.nz/documents/about-msd-and-our-work/publications-resources/official-information-responses/2022/july/07072022-requesting-the-document-automated-decision-making-in-msd-proposed-legislative-and-policy-framework-document-.pdf) | March 1, 2023\n* [Callaghan Innovation, EU AI Fact Sheet 4, High-risk AI systems](https://www.callaghaninnovation.govt.nz/assets/documents/Resource-EU-AI-Act-Support/EU-AI-Policy-Fact-Sheet-4-High-Risk-AI-Systems.pdf)\n* [Discussion Paper: International Data Ethics Frameworks](https://data.govt.nz/assets/Uploads/Discussion-paper-International-data-ethics-frameworks-March-2020.pdf) | Prepared on behalf of the Government Chief Data Steward for the Data Ethics Advisory Group (DEAG)\n* [Government Use of Artificial Intelligence in New Zealand: Final Report on Phase 1 of the New Zealand Law Foundation's Artificial Intelligence and Law in New Zealand Project](https://www.data.govt.nz/assets/data-ethics/algorithm/NZLF-report.pdf) | 2019\n* [Initial advice on Generative Artificial Intelligence in the public service](https://www.digital.govt.nz/assets/Standards-guidance/Technology-and-architecture/Generative-AI/Joint-System-Leads-tactical-guidance-on-public-service-use-of-GenAI-September-2023.pdf) | July 2023\n* [New Zealand Income Insurance: service model and automated decision making](https://www.mbie.govt.nz/dmsdocument/26492-nzii-service-model-and-adm-pdf) | Ministry of Business, Innovation & Employment\n* [New Zealand's Strategy for Artificial Intelligence: Investing with confidence](https://www.mbie.govt.nz/assets/new-zealands-strategy-for-artificial-intelligence.pdf) | Ministry of Business, Innovation & Employment, Accelerating Private Sector AI Adoption and Innovation, July 2025\n* [Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods](https://www.data.govt.nz/assets/Uploads/Public-Scrutiny-of-Automated-Decisions.pdf) | An Upturn and Omidyar Network Report\n\n#### Nigeria\n\n* [NAIS National Artificial Intelligence Strategy](https://ncair.nitda.gov.ng/wp-content/uploads/2024/08/National-AI-Strategy_01082024-copy.pdf) | National Center for Artificial Intelligence & Robotics (NCAIR) and the National Information Technology Development Agency (NITDA), August 2024\n\n#### Norway\n\n* [Artificial Intelligence and Democratic Elections — International Experiences and National Recommendations](https://www.regjeringen.no/contentassets/23f8fd1726634f589724d96b49fe994c/en_rapport-ekspertgruppe-ki-og-valg.pdf) | Ministry of Local Government and Regional Development, Expert Group on Artificial Intelligence and Elections, February 2025\n* [National Strategy for Artificial Intelligence](https://www.regjeringen.no/contentassets/1febbbb2c4fd4b7d92c67ddd353b6ae8/en-gb/pdfs/ki-strategi_en.pdf) | Ministry of Local Government and Modernisation\n\n#### Philippines\n\n#### Sierra Leone\n\n* [Artificial Intelligence and Automation: Preserving Human Agency in a World of Automation](https://statehouse.gov.sl/wp-content/uploads/2025/01/H.E.KEYNOTE-ACE-EDUCATION-WEEK-2025-JAN-2025-3.pdf) | Keynote Statement on the Annual Celebration of Education Week (ACE Week) 2025 by His Excellency, Dr. Julius Maada Bio, President of the Republic of Sierra Leone at Makeni, Sierra Leone, Friday, 24th January 2025\n\n#### Singapore\n\n* [Artificial Intelligence Model Risk Management: Observations from a Thematic Review](https://www.mas.gov.sg/-/media/mas-media-library/publications/monographs-or-information-paper/imd/2024/information-paper-on-ai-risk-management-final.pdf) | Monetary Authority of Singapore, Information Paper, December 2024\n* [Guide for Using Generative AI in the Legal Sector](https://www.mlaw.gov.sg/files/Guide_for_Using_Generative_AI_in_the_Legal_Sector.pdf) | Draft for Public Consultation (1 September to 30 September 2025)\n* [National Artificial Intelligence Strategy: Advancing Our Smart Nation Journey](https://file.go.gov.sg/nais2019.pdf) | Smart Nation Singapore, November 2019\n* Personal Data Protection Commission (PDPC)\n  * [Companion to the Model AI Governance Framework – Implementation and Self-Assessment Guide for Organizations](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/resource-for-organisation/ai/sgisago.pdf)\n  * [Compendium of Use Cases: Practical Illustrations of the Model AI Governance Framework](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/resource-for-organisation/ai/sgaigovusecases.pdf)\n  * [Model Artificial Intelligence Governance Framework](https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/SGModelAIGovFramework2.pdf) |  (Second Edition)\n  * [Privacy Enhancing Technology: Proposed Guide on Synthetic Data Generation](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/other-guides/proposed-guide-on-synthetic-data-generation.pdf)\n* [The Singapore Consensus on Global AI Safety Research Priorities](https://aisafetypriorities.org/)\n\n#### Slovakia\n\n* [2030 Digital Transformation Strategy for Slovakia: Strategy for transformation of Slovakia into a successful digital country](https://mirri.gov.sk/wp-content/uploads/2019/10/SDT-English-Version-FINAL.pdf)\n* [Analýza a návrh možností výskumu, vývoja a aplikácie umelej inteligencie na Slovensku – Dielo č. 2: Manuál pre firmy na zavedenie umelej inteligencie](https://mirri.gov.sk/wp-content/uploads/2020/03/Dielo2-Manual.pdf) | Slovak University of Technology & Office of the Deputy Prime Minister for Investments and Informatization, Manual for Companies on Implementing Artificial Intelligence, December 11, 2019\n* [Analýza a návrh možností výskumu, vývoja a aplikácie umelej inteligencie na Slovensku](https://mirri.gov.sk/wp-content/uploads/2020/03/Brozura-Umela-Inteligencia-A4-LRS.pdf) | Slovak University of Technology & Office of the Deputy Prime Minister for Investments and Informatization, Analysis and Proposal for Research, Development, and Application of Artificial Intelligence in Slovakia, March 2020\n* [Preliminary position of the Slovak Republic on the “White Paper on Artificial Intelligence – A European approach to excellence and trust”](https://mirri.gov.sk/wp-content/uploads/2020/10/Preliminary-position-of-The-Slovak-Republic-on-the-%E2%80%9CWhite-Paper-on-Artificial-Intelligence-%E2%80%93-A-European-approach-to-excellence-and-trust%E2%80%9Ddr.pdf) | Ministry of Investments, Regional Development and Informatization (MIRRI), National Position Paper on the EU AI White Paper, October 2020\n* [Umelá inteligencia](https://mirri.gov.sk/sekcie/investicie/digitalne-inovacie/umela-inteligencia/) | Ministry of Investments, Regional Development and Informatization (MIRRI), Slovak government portal on Artificial Intelligence policy, ethics, and innovation framework\n* [Umelá inteligencia vo vzdelávaní: Plán zodpovedného využívania AI vo vzdelávaní na Slovensku 2025–2027](https://www.minedu.sk/data/att/803/34353.721f01.pdf) | Ministry of Education, Research, Development and Youth of the Slovak Republic, Plan for the Responsible Use of AI in Education in Slovakia 2025–2027\n\n\n#### Slovenia\n\n* [Akcijski načrt strategije Digitalna Slovenija 2030](https://www.gov.si/assets/ministrstva/MDP/DDD-dokumenti/Akcijski-nacrt-strategije-Digitalna-Slovenija-2030.pdf) | Action Plan for the Digital Slovenia 2030 Strategy, National Strategic Plan for the Digital Decade 2030\n* [Zaveze za uporabo orodij generativne umetne inteligence, dostopnih na spletu](https://nio.gov.si/sl/products/priporocila%2Bjavnim%2Busluzbencem%2Bpri%2Buporabi%2Borodij%2Bgenerativne%2Bumetne%2Binteligence%2Bdostopnih%2Bna%2Bspletu) | Commitments for the Use of Generative Artificial Intelligence Tools Available Online, October 24, 2024\n* [Digital Public Services Strategy 2030](https://www.gov.si/assets/ministrstva/MDP/Digital_Public_Services_Strategy_2030.pdf)\n* [Digitalna Slovenija 2030](https://www.gov.si/assets/ministrstva/MDP/Dokumenti/DSI2030-potrjena-na-Vladi-RS_marec-2023.pdf) | Digital Slovenia 2030: National Strategy for Digital Transformation to 2030, March 2023\n* [National Programme to Promote the Development and Use of Artificial Intelligence in the Republic of Slovenia by 2025 NpAI](https://www.gov.si/assets/ministrstva/MDP/National_Programme_for_AI_2025.pdf)\n* [Register rabe UI](https://registerui.djnd.si/) | Slovenia's Register of AI Use\n\n#### South Africa\n\n* [Computer Applications Technology: Learner Guidelines for Practical Assessment Tasks, Grade 12, 2025](https://www.education.gov.za/Portals/0/CD/2025%20PATs/Computer%20Applications%20Technology%20PAT%20GR%2012%202025%20Learner%20Guidelines%20Eng.pdf?ver=2025-02-11-163720-737) | Department of Basic Education\n* [South Africa's Artificial Intelligence Planning: Adoption of AI by Government](https://www.dcdt.gov.za/images/phocadownload/AI_Government_Summit/National_AI_Government_Summit_Discussion_Document.pdf) | Department of Communications & Digital Technologies and the Artificial Intelligence Institute of South Africa, October 2023\n\n#### South Korea\n\n* [AI Safety Institute of Korea](https://www.aisi.re.kr/eng)\n* [Basic Act on the Promotion of Artificial Intelligence Development and Establishment of a Trust Framework](https://likms.assembly.go.kr/bill/billDetail.do?billId=PRC_R2V4H1W1T2K5M1O6E4Q9T0V7Q9S0U0) | National Assembly, 인공지능 발전과 신뢰 기반 조성 등에 관한 기본법안, (대안,Alternative Draft)\n* [인공지능 발전과 신뢰 기반 조성 등에 관한 기본법](https://www.law.go.kr/%25EB%25B2%2595%25EB%25A0%25B9/%25EC%259D%25B8%25EA%25B3%25B5%25EC%25A7%2580%25EB%258A%25A5%2520%25EB%25B0%259C%25EC%25A0%2584%25EA%25B3%25BC%2520%25EC%258B%25A0%25EB%25A2%25B0%2520%25EA%25B8%25B0%25EB%25B0%2598%2520%25EC%25A1%25B0%25EC%2584%25B1%2520%25EB%2593%25B1%25EC%2597%2590%2520%25EA%25B4%2580%25ED%2595%259C%2520%25EA%25B8%25B0%25EB%25B3%25B8%25EB%25B2%2595/%2820676,20250121%29) | Ministry of Science and ICT, Framework Act on the Development of Artificial Intelligence and Creation of a Trust Foundation, January 21, 2025\n* [생성형 인공지능(AI) 개발·활용을 위한 개인정보 처리 안내서(안)](https://www.aitimes.kr/news/download.php?subUploadDir=202508/&savefilename=35952_300.pdf&filename=%EC%83%9D%EC%84%B1%ED%98%95%20%EC%9D%B8%EA%B3%B5%EC%A7%80%EB%8A%A5(AI)%20%EA%B0%9C%EB%B0%9C%C2%B7%ED%99%9C%EC%9A%A9%EC%9D%84%20%EC%9C%84%ED%95%9C%20%EA%B0%9C%EC%9D%B8%EC%A0%95%EB%B3%B4%20%EC%B2%98%EB%A6%AC%20%EC%95%88%EB%82%B4%EC%84%9C.pdf&idxno=300) | Personal Information Protection Commission, Guidelines on Personal Information Processing for the Development and Utilization of Generative Artificial Intelligence (Draft), August 2025\n\n#### Sweden\n\n* [Främja den offentliga förvaltningens förmåga att använda AI](https://www.digg.se/download/18.129a4fef1939e2e1c1f240b2/1647952779554/framja-den-offentliga-forvaltningens-formaga-att-anvanda-ai.pdf)\n* [GDPR och AI](https://www.imy.se/verksamhet/ai/gdpr-och-ai/)\n* [Myndigheterna och AI](https://www.statskontoret.se/siteassets/rapporter-pdf/2024/oos_51---utskriftsversion.pdf)\n* [Nationella AI-uppdraget: AI-guide, förtroendemodell m.m.](https://www.skatteverket.se/download/18.21e4ba96188260715e391c/1684914447566/nationella-ai-uppdraget.pdf) | Skatteverket (Swedish Tax Agency)\n* [Riktlinjer för användning av generativ AI och kompetenshöjande insatser](https://www.digg.se/download/18.41bb5ce61939e3df66831ea/1737390380051/Rapport%20Riktlinjer%20f%C3%B6r%20anv%C3%A4ndning%20av%20generativ%20AI%20och%20kompetensh%C3%B6jande%20insatser%20(Fi2024-01535).pdf)\n* [Sweden AI Strategy Report](https://ai-watch.ec.europa.eu/countries/sweden/sweden-ai-strategy-report_en) | European Commission\n\n#### Switzerland\n\n* [Digital Switzerland Strategy 2025](https://digital.swiss/userdata/uploads/strategie-dch-en.pdf)\n\n#### Tanzania\n\n* [Artificial Intelligence Readiness Assessment Report](https://tanzania.un.org/sites/default/files/2025-07/National%20AI%20Readiness%20Report.pdf) | UNESCO, 2025\n\n#### Ukraine\n\n* Ministry of Digital Transformation\n  * [White Paper on Artificial Intelligence Regulation in Ukraine: Vision of the Ministry of Digital Transformation of Ukraine](https://thedigital.gov.ua/storage/uploads/files/page/community/docs/%D0%91%D1%96%D0%BB%D0%B0_%D0%BA%D0%BD%D0%B8%D0%B3%D0%B0_%D0%B7_%D1%80%D0%B5%D0%B3%D1%83%D0%BB%D1%8E%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D0%A8%D0%86_%D0%B2_%D0%A3%D0%BA%D1%80%D0%B0%D1%97%D0%BD%D1%96_%D0%90%D0%9D%D0%93%D0%9B.pdf) | Version for Consultation\n  * [Дорожня карта з регулювання штучного інтелекту в Україні: Bottom-Up Підхід](https://cms.thedigital.gov.ua/storage/uploads/files/page/community/docs/%D0%94%D0%BE%D1%80%D0%BE%D0%B6%D0%BD%D1%8F_%D0%BA%D0%B0%D1%80%D1%82%D0%B0_%D0%B7_%D1%80%D0%B5%D0%B3%D1%83%D0%BB%D1%8E%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D0%A8%D0%86_%D0%B2_%D0%A3%D0%BA%D1%80%D0%B0%D1%97%D0%BD%D1%96_compressed.pdf)\n* [Guidelines on the Responsible Use of Artificial Intelligence in the News Media](https://webportal.nrada.gov.ua/wp-content/uploads/2024/05/Ukraine-AI-Guidelines-for-Media.pdf) | Ministry of Digital Transformation, Ministry of Culture and Information Policy, and National Council of Television and Radio Broadcasting\n\n#### United Kingdom\n\n* [AI and the Law: A Discussion Paper](https://cloud-platform-e218f50a4812967ba1215eaecede923f.s3.amazonaws.com/uploads/sites/54/2025/07/AI-paper-PDF.pdf) | Law Commission, 2025\n* [AI Safety Institute, Safety cases at AISI](https://www.aisi.gov.uk/work/safety-cases-at-aisi) | (AISI)\n* [Artificial Intelligence Playbook for the UK Government](https://assets.publishing.service.gov.uk/media/67aca2f7e400ae62338324bd/AI_Playbook_for_the_UK_Government__12_02_.pdf) | Government Digital Service and Department for Science, Innovation & Technology, February 2025\n* [Beginner's guide to measurement GPG118](https://www.npl.co.uk/gpgs/beginners-guide-to-measurement) | National Physical Laboratory (NPL)\n* Department for Science, Innovation and Technology\n  * [The Bletchley Declaration by Countries Attending the AI Safety Summit](https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration/the-bletchley-declaration-by-countries-attending-the-ai-safety-summit-1-2-november-2023) | 1-2 November 2023\n  * [Evaluation of the Cyber AI Hub programme | January 8, 2025](https://www.gov.uk/government/publications/evaluation-of-the-northern-ireland-cyber-ai-hub-programme/evaluation-of-the-cyber-ai-hub-programme)\n  * [Frontier AI: capabilities and risks](https://www.gov.uk/government/publications/frontier-ai-capabilities-and-risks-discussion-paper)\n  * [Generative Artificial Intelligence in the Education System](https://www.niassembly.gov.uk/globalassets/documents/raise/publications/2022-2027/2025/education/2725.pdf) | Northern Ireland Assembly, Research and Information Service, March 20, 2025\n  * [Global Coalition on Telecommunications: principles on AI adoption in the telecommunications industry](https://www.gov.uk/government/publications/global-coalition-on-telecommunications-principles-on-ai-adoption-in-the-telecommunications-industry/global-coalition-on-telecommunications-principles-on-ai-adoption-in-the-telecommunications-industry) | January 16, 2025\n  * [Introduction to AI Assurance](https://www.gov.uk/government/publications/introduction-to-ai-assurance)\n* [Information Commissioner's Office, AI tools in recruitment](https://ico.org.uk/action-weve-taken/audits-and-overview-reports/ai-tools-in-recruitment/) | ICO, November 6, 2024\n* [International Scientific Report on the Safety of Advanced AI](https://www.gov.uk/government/publications/international-scientific-report-on-the-safety-of-advanced-ai) | Department for Science, Innovation and Technology and AI Safety Institute\n* [Media literacy](https://publications.parliament.uk/pa/ld5901/ldselect/ldcomm/163/163.pdf) | House of Lords Communications and Digital Committee, 3rd Report of Session 2024-25\n* [Northern Ireland response to the AI Council AI Roadmap](https://matrixni.org/wp-content/uploads/2021/04/NI-Response-to-UK-AI-roadmap.pdf)\n* [Online Harms White Paper: Full government response to the consultation](https://www.gov.uk/government/consultations/online-harms-white-paper)\n* [Parliamentary Office of Science and Technology](https://researchbriefings.files.parliament.uk/documents/POST-PN-0735/POST-PN-0735.pdf) | (POST), POSTnote 735, Energy Security and AI\n* [The Public Sector Bodies Accessibility Regulations 2018](https://www.legislation.gov.uk/uksi/2018/852/contents/made) | (Websites and Mobile Applications)\n* [Red Teaming for GenAI Harms: Revealing the Risks and Rewards for Online Safety | July 23, 2024](https://www.ofcom.org.uk/siteassets/resources/documents/consultations/discussion-papers/red-teaming/red-teaming-for-gen-ai-harms.pdf?v=370762) | Ofcom\n* [The safe and effective use of AI in education: Leadership toolkit video transcripts](https://assets.publishing.service.gov.uk/media/6842e04ee5a089417c8060c5/Leadership_Toolkit_-_Transcript.pdf) | Department of Education, June 2025\n* [Trusted third-party AI assurance roadmap](https://www.gov.uk/government/publications/trusted-third-party-ai-assurance-roadmap/trusted-third-party-ai-assurance-roadmap) | Department for Science, Innovation & Technology, September 3, 2025\n* [US AISI and UK AISI Joint Pre-Deployment Test: Anthropic's Claude 3.5 Sonnet](https://www.nist.gov/system/files/documents/2024/11/19/Upgraded%20Sonnet-Publication-US.pdf) | October 2024 Release\n* [US AISI and UK AISI Joint Pre-Deployment Test: OpenAI o1](https://www.nist.gov/system/files/documents/2024/12/18/US_UK_AI%20Safety%20Institute_%20December_Publication-OpenAIo1.pdf) | December 2024\n* [Use of AI in Legislatures](https://www.niassembly.gov.uk/globalassets/documents/raise/publications/2022-2027/2025/clg/3325.pdf) | Northern Ireland Assembly, September 2024\n\n#### United States (Federal Government)\n\n**Bureau of Labor Statistics**\n* [Bureau of Labor Statistics Report to the Committees on Appropriations of the House of Representatives and the Senate on Measuring the Effects of New Technologies on the American Workforce](https://www.bls.gov/bls/congressional-reports/measuring-the-effects-of-new-technologies-on-the-american-workforce.pdf)\n* [Incorporating AI impacts in BLS employment projections: occupational case studies](https://www.bls.gov/opub/mlr/2025/article/incorporating-ai-impacts-in-bls-employment-projections.htm) | February 2025\n\n**Consumer Financial Protection Bureau (CFPB)**  \n* [12 CFR Part 1002 - Equal Credit Opportunity Act](https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1002/) | (Regulation B)\n* [Chatbots in consumer finance](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/)\n* [Innovation spotlight: Providing adverse action notices when using AI/ML models](https://www.consumerfinance.gov/about-us/blog/innovation-spotlight-providing-adverse-action-notices-when-using-ai-ml-models/)\n\n**Commodity Futures Trading Commission (CFTC)**  \n* [A Primer on Artificial Intelligence in Securities Markets](https://www.cftc.gov/media/2846/LabCFTC_PrimerArtificialIntelligence102119/download)\n* [Responsible Artificial Intelligence in Financial Markets](https://www.cftc.gov/PressRoom/PressReleases/8905-24)\n\n**Congressional Budget Office**\n* [H.R. 9720, AI Incident Reporting and Security Enhancement Act](https://www.cbo.gov/system/files/2024-10/hr9720.pdf)\n\n**Congressional Research Service**\n* [Artificial Intelligence in Health Care](https://crsreports.congress.gov/product/pdf/R/R48319) | December 30, 2024\n* [Artificial Intelligence and Machine Learning in Financial Services](https://crsreports.congress.gov/product/pdf/R/R47997) | April 3, 2024\n* [Artificial Intelligence: Background, Selected Issues, and Policy Considerations](https://crsreports.congress.gov/product/pdf/R/R46795) | May 19, 2021\n* [Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th Congress](https://www.energy.gov/sites/default/files/2023-09/Artificial%20Intelligence%20Overview%2C%20Recent%20Advances%2C%20and%20Considerations%20for%20the%20118th%20Congress.pdf) | August 4, 2023\n* [Highlights of the 2023 Executive Order on Artificial Intelligence for Congress](https://crsreports.congress.gov/product/pdf/R/R47843/2) | November 17, 2023\n\n**Copyright Office**\n* [Copyright and Artificial Intelligence Part 1 Digital Replicas](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-1-Digital-Replicas-Report.pdf) |  July 2024\n* [Copyright and Artificial Intelligence Part 2 Copyrightability](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf) |  January 2025\n* [Copyright and Artificial Intelligence Part 3 Generative AI Training](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf) | May 2025\n\n**Data.gov**\n* [Privacy Policy and Data Policy](https://data.gov/privacy-policy/)\n\n**Defense Advanced Research Projects Agency (DARPA)**\n* [Explainable Artificial Intelligence](https://www.darpa.mil/program/explainable-artificial-intelligence) |  (XAI) (Archived)\n\n**Defense Technical Information Center**  \n* [Computer Security Technology Planning Study](https://apps.dtic.mil/sti/citations/AD0758206) | October 1, 1972\n\n**Department of Agriculture (USDA)**\n* [Fiscal Year 2025-2026 AI Strategy](https://www.usda.gov/sites/default/files/documents/fy-2025-2026-usda-ai-strategy.pdf)\n\n**Department of Commerce**\n* [Artificial intelligence](https://www.commerce.gov/issues/artificial-intelligence)\n* [Bureau of Industry and Security](https://www.bis.gov/)\n  * [Department of Commerce Rescinds Biden-Era Artificial Intelligence Diffusion Rule, Strengthens Chip-Related Export Controls](https://media.bis.gov/sites/default/files/documents/05.07%20Recission%20of%20AI%20Diffusion%20Press%20Release.pdf) | Bureau of Industry and Security, May 12, 2025\n  * [Framework for Artificial Intelligence Diffusion](https://public-inspection.federalregister.gov/2025-00636.pdf)\n* [Evaluation of DeepSeek AI Models](https://www.nist.gov/system/files/documents/2025/09/30/CAISI_Evaluation_of_DeepSeek_AI_Models.pdf) | Center for AI Standards and Innovation, September 30, 2025\n* [Intellectual property](https://www.commerce.gov/issues/intellectual-property)\n* [National Telecommunications and Information Administration](https://www.ntia.gov/) | (NTIA)\n  * [AI Accountability Policy Report](https://www.ntia.gov/issues/artificial-intelligence/ai-accountability-policy-report)\n  * [AI System Documentation](https://www.ntia.gov/issues/artificial-intelligence/ai-accountability-policy-report/developing-accountability-inputs-a-deeper-dive/information-flow/ai-system-documentation)\n  * [Internet Policy Task Force, Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy Framework](https://www.ntia.doc.gov/files/ntia/publications/iptf_privacy_greenpaper_12162010.pdf)\n  * [NTIA Artificial Intelligence Accountability Policy Report](https://www.ntia.gov/sites/default/files/publications/ntia-ai-report-final.pdf) | March 2024\n* [National Institute of Standards and Technology](https://www.nist.gov/)\n  * [A Possible Approach for Evaluating AI Standards Development](https://nvlpubs.nist.gov/nistpubs/gcr/2026/NIST.GCR.26-069.pdf)\n  * [AI Risk Management Framework Concept Paper](https://www.nist.gov/document/airmfconceptpaper)\n  * [AI Risk Management Framework: Initial Draft](https://www.nist.gov/document/ai-risk-management-framework-initial-draft)\n  * [AI Use Taxonomy: A Human-Centered Approach](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.200-1.pdf) | NIST AI 200-1\n  * [Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf) | NIST AI 100-2e2023\n    * [Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations updated](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf) | NIST AI 100-2e2025\n  * [Artificial Intelligence Risk Management Framework](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf) | NIST AI 100-1 (NIST AI RMF 1.0)\n  * [Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) | NIST AI 600-1\n  * [Assessing Risks and Impacts of AI (ARIA): Pilot Evaluation Report](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.700-2.pdf) | NIST AI 700-2\n  * [Challenges to the Monitoring of Deployed AI Systems](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf) | NIST AI 800-4\n  * [Cybersecurity Framework Profile for Artificial Intelligence](https://nvlpubs.nist.gov/nistpubs/ir/2025/NIST.IR.8596.iprd.pdf) | NIST IR 8596 (initial preliminary draft)\n  * [De-Identifying Government Datasets: Techniques and Governance](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-188.pdf) | NIST SP 800-188\n  * [Four Principles of Explainable Artificial Intelligence](https://nvlpubs.nist.gov/nistpubs/ir/2021/nist.ir.8312.pdf) | NISTIR 8312\n  * [Guide for Conducting Risk Assessments](https://nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-30r1.pdf) | NIST Special Publication 800-30 Revision 1\n  * [Managing Misuse Risk for Dual-Use Foundation Models](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-1.ipd2.pdf) | NIST AI 800-1 (2nd public draft)\n  * [NIST AI RMF Playbook](https://airc.nist.gov/airmf-resources/playbook/)\n  * [NIST AI RMF Roadmap](https://airc.nist.gov/airmf-resources/roadmap/)\n  * [NIST/SEMATECH e-Handbook of Statistical Methods](https://www.itl.nist.gov/div898/handbook/)\n  * [Psychological Foundations of Explainability and Interpretability in Artificial Intelligence](https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8367.pdf)\n  * [Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-4.pdf) | NIST AI 100-4\n  * [Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-218A.pdf) | NIST SP 800-218A\n  * [Simple Guide for Evaluating and Expressing the Uncertainty of NIST Measurement Results](https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.1900.pdf) | NIST Technical Note 1900\n    * [International Bureau of Weights and Measures, Evaluation of measurement data—Guide to the expression of uncertainty in measurement](https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6) | (BIPM)\n  * [The Language of Trustworthy AI: An In-Depth Glossary of Terms](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-3.pdf) | NIST AI 100-3\n    * [NIST AIRC Glossary portal](https://airc.nist.gov/glossary/)\n    * [The Language of Trustworthy AI: An In-Depth Glossary of Terms, spreadsheet](https://docs.google.com/spreadsheets/d/e/2PACX-1vTRBYglcOtgaMrdF11aFxfEY3EmB31zslYI4q2_7ZZ8z_1lKm7OHtF0t4xIsckuogNZ3hRZAaDQuv_K/pubhtml)\n  * [Towards a Standard for Identifying and Managing Bias in Artificial Intelligence](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf) | NIST SP 1270\n  * [U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools](https://www.nist.gov/document/report-plan-federal-engagement-developing-technical-standards-and-related-tools)\n  * [2024 NIST GenAI (Pilot Study): Text-to-Text Evaluation Overview and Results](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.700-1.pdf) | NIST AI 700-1\n* National Oceanic and Atmospheric Administration (NOAA)\n  * [NOAA Artificial Intelligence Strategy: Analytics for Next-Generation Earth Science](https://sciencecouncil.noaa.gov/wp-content/uploads/2023/04/2020-AI-Strategy.pdf) | February 2020\n* Office of the Under Secretary for Economic Affairs\n  * [Generative Artificial Intelligence and Open Data](https://www.commerce.gov/sites/default/files/2025-01/GenerativeAI-Open-Data.pdf) | Guidelines and Best Practices, Version 1, January 16, 2025\n* [Outline: Proposed Zero Draft for a Standard on AI Testing, Evaluation, Verification, and Validation](https://www.nist.gov/system/files/documents/2025/07/15/Outline_%20Proposed%20Zero%20Draft%20for%20a%20Standard%20on%20AI%20TEVV-for-web.pdf)\n* [SP 800-53 Control Overlays for Securing AI Systems](https://csrc.nist.gov/csrc/media/Projects/cosais/documents/NIST-Overlays-SecuringAI-concept-paper.pdf) | NIST\n* [U.S. Artificial Intelligence Safety Institute](https://www.nist.gov/aisi) | (USAISI)\n  * [US AISI and UK AISI Joint Pre-Deployment Test: Anthropic's Claude 3.5 Sonnet](https://www.nist.gov/system/files/documents/2024/11/19/Upgraded%20Sonnet-Publication-US.pdf) | October 2024 Release\n  * [US AISI and UK AISI Joint Pre-Deployment Test: OpenAI o1](https://www.nist.gov/system/files/documents/2024/12/18/US_UK_AI%20Safety%20Institute_%20December_Publication-OpenAIo1.pdf) | December 2024\n\n**Department of Defense**  \n* [AI Data Security](https://media.defense.gov/2025/May/22/2003720601/-1/-1/0/CSI_AI_DATA_SECURITY.PDF) | Joint Cybersecurity Information, Best Practices for Securing Data Used to Train & Operate AI Systems, May 2025, Ver. 1.0\n* [AI Principles: Recommendations on the Ethical Use of Artificial Intelligence](https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF)\n* [Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology](https://media.defense.gov/2020/Jul/01/2002347967/-1/-1/1/DODIG-2020-098.PDF)\n* [Content Credentials: Strengthening Multimedia Integrity in the Generative AI Era](https://media.defense.gov/2025/Jan/29/2003634788/-1/-1/0/CSI-CONTENT-CREDENTIALS.PDF) | January 2025\n* [Chief Data and Artificial Intelligence Officer Assessment and Assurance](https://gitlab.jatic.net/home) | (CDAO)\n    * [RAI Toolkit](https://rai.tradewindai.com/)\n* Department of the Army\n  * [Proceedings of the Thirteenth Annual U.S. Army Operations Research Symposium](https://apps.dtic.mil/sti/pdfs/ADA007126.pdf) | Volume 1, October 29 to November 1, 1974\n* [Guidelines for secure AI system development](https://media.defense.gov/2023/Nov/27/2003346994/-1/-1/0/GUIDELINES-FOR-SECURE-AI-SYSTEM-DEVELOPMENT.PDF)\n* [U.S. Department of Defense Responsible Artificial Intelligence Strategy and Implementation Pathway](https://media.defense.gov/2022/Jun/22/2003022604/-1/-1/0/Department-of-Defense-Responsible-Artificial-Intelligence-Strategy-and-Implementation-Pathway.PDF) | June 2022\n\n**Department of Education**\n* [Inventory of U.S. Department of Education AI Use Cases](https://www.ed.gov/about/ed-overview/artificial-intelligence-ai-guidance)\n* [Office of Educational Technology](https://tech.ed.gov/)\n  * [Designing for Education with Artificial Intelligence: An Essential Guide for Developers](https://tech.ed.gov/designing-for-education-with-artificial-intelligence/)\n  * [Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration](https://tech.ed.gov/education-leaders-ai-toolkit/) | October 2024\n\n**Department of Energy**\n* [Artificial Intelligence and Technology Office](https://www.energy.gov/ai/artificial-intelligence-technology-office)\n  * [AI Risk Management Playbook](https://www.energy.gov/ai/doe-ai-risk-management-playbook-airmp) | (AIRMP)\n  * [AI Use Case Inventory](https://www.energy.gov/sites/default/files/2023-07/DOE_2023_AI_Use_Case_Inventory_0.pdf) | (DOE Use Cases Releasable to Public in Accordance with E.O. 13960)\n  * [Digital Climate Solutions Inventory](https://www.energy.gov/sites/default/files/2022-09/Digital_Climate_Solutions_Inventory.pdf)\n  * [Generative Artificial Intelligence Reference Guide](https://www.energy.gov/sites/default/files/2024-06/Generative%20AI%20Reference%20Guide%20v2%206-14-24.pdf)\n\n**Department of Health and Human Services**\n* [Strategic Plan for the Use of Artificial Intelligence in Health Human Services and Public Health Strategic Plan](https://irp.nih.gov/system/files/media/file/2025-03/2025-hhs-ai-strategic-plan_full_508.pdf) | January 2025\n\n**Department of Homeland Security**  \n* [Acquisition and Use of Artificial Intelligence and Machine Learning Technologies by DHS Components](https://www.dhs.gov/sites/default/files/2023-09/23_0913_mgmt_139-06-acquistion-use-ai-technologies-dhs-components.pdf) | Policy Statement 139-06, August 8, 2023\n* [Artificial Intelligence and Autonomous Systems](https://www.dhs.gov/science-and-technology/artificial-intelligence)\n* [Artificial Intelligence Safety and Security Board](https://www.dhs.gov/artificial-intelligence-safety-and-security-board)\n* [Department of Homeland Security Artificial Intelligence Roadmap 2024](https://www.dhs.gov/sites/default/files/2024-03/24_0315_ocio_roadmap_artificialintelligence-ciov3-signed-508.pdf)\n* [DHS Has Taken Steps to Develop and Govern Artificial Intelligence, But More Action is Needed to Ensure Appropriate Use](https://www.oig.dhs.gov/sites/default/files/assets/2025-02/OIG-25-10-Jan25.pdf) | Office of Inspector General, OIG-25-10, January 30, 2025\n* [DHS Playbook for Public Sector Generative Artificial Intelligence Deployment](https://www.dhs.gov/sites/default/files/2025-01/25_0106_ocio_dhs-playbook-for-public-sector-generative-artificial-intelligence-deployment-508-signed.pdf) | January 2025\n* [Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure](https://www.dhs.gov/sites/default/files/2024-11/24_1114_dhs_ai-roles-and-responsibilities-framework-508.pdf) | November 14, 2024\n* [Safety and Security Guidelines for Critical Infrastructure Owners and Operators](https://www.dhs.gov/publication/safety-and-security-guidelines-critical-infrastructure-owners-and-operators)\n* [The Department of Homeland Security Simplified Artificial Intelligence Use Case Inventory](https://www.dhs.gov/ai/use-case-inventory)\n  * [AI at DHS: A Deep Dive into our Use Case Inventory](https://www.dhs.gov/news/2024/12/16/ai-dhs-deep-dive-our-use-case-inventory)\n* [Use of Commercial Generative Artificial Intelligence Tools](https://www.dhs.gov/sites/default/files/2023-11/23_1114_cio_use_generative_ai_tools.pdf)\n\n**Department of Justice**  \n* [Artificial Intelligence Strategy for the U.S. Department of Justice](https://www.justice.gov/d9/pages/attachments/2021/02/04/doj_artificial_intelligence_strategy_december_2020.pdf) | December 2020\n* [Civil Rights Division, Artificial Intelligence and Civil Rights](https://www.justice.gov/crt/ai)\n* [Privacy Act of 1974](https://www.justice.gov/opcl/privacy-act-1974)\n  * [Overview of The Privacy Act of 1974](https://www.justice.gov/opcl/overview-privacy-act-1974-2020-edition) | (2020 Edition)\n* [Shaping the Department's Artificial Intelligence Efforts 2021-2025](https://www.justice.gov/archives/media/1385331/dl?inline)\n\n**Department of Labor**\n* [Artificial Intelligence Use Case Inventory](https://www.dol.gov/agencies/oasam/centers-offices/ocio/ai-inventory)\n* [Validation of Employee Selection Procedures](https://web.archive.org/web/20250103095140/https://www.dol.gov/agencies/ofccp/faqs/employee-selection-procedures) | Office of Federal Contract Compliance Programs (archived)\n\n**Department of State**\n* [Artificial Intelligence](https://www.state.gov/artificial-intelligence/)\n* [AI Inventory 2024](https://2021-2025.state.gov/department-of-state-ai-inventory-2024/) |  (Archived Content)\n* [Enterprise Artificial Intelligence Strategy FY2024-FY-2025 Empowering Diplomacy through Responsible AI](https://www.state.gov/wp-content/uploads/2023/11/Department-of-State-Enterprise-Artificial-Intelligence-Strategy.pdf) | October 2023\n\n**Department of the Treasury**  \n* Internal Revenue Service (IRS)\n  * [Interim Policy for AI Governance](https://www.irs.gov/pub/foia/ig/spder/raas-10-0325-0001-public.pdf) | March 11, 2025\n* [Managing Artificial Intelligence-Specific Cybersecurity Risks in the Financial Services Sector](https://home.treasury.gov/system/files/136/Managing-Artificial-Intelligence-Specific-Cybersecurity-Risks-In-The-Financial-Services-Sector.pdf) | March 2024\n\n**Department of Veterans Affairs**\n* [Building the Future: VA’s Strategy for Adopting High-Impact Artificial Intelligence to Improve Services for Veterans](https://department.va.gov/ai/building-the-future-vas-strategy-for-adopting-high-impact-artificial-intelligence-to-improve-services-for-veterans/)\n\n**Equal Employment Opportunity Commission (EEOC)**\n* [EEOC Letter](https://www.bennet.senate.gov/public/_cache/files/0/a/0a439d4b-e373-4451-84ed-ba333ce6d1dd/672D2E4304D63A04CC3465C3C8BF1D21.letter-to-chair-dhillon.pdf) | from U.S. senators re: hiring software\n* [Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines)\n\n**Executive Office of the President of the United States**\n* [Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy, February 2012](https://obamawhitehouse.archives.gov/sites/default/files/privacy-final.pdf) | Obama White House Archives\n* [Fact Sheet Eliminating Barriers for Federal Artificial Intelligence Use and Procurement](https://www.whitehouse.gov/wp-content/uploads/2025/02/AI-Memo-Fact-Sheet.pdf)\n* [Framework to Advance AI Governance and Risk Management in National Security](https://ai.gov/wp-content/uploads/2024/10/NSM-Framework-to-Advance-AI-Governance-and-Risk-Management-in-National-Security.pdf)\n* [Winning the Race: America's AI Action Plan](https://whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf) | July 2025\n\n**Federal Aviation Administration (FAA)**\n* [Roadmap for Artificial Intelligence Safety Assurance](https://www.faa.gov/media/82891) | Version I, July 23, 2024\n\n**Federal Deposit Insurance Corporation (FDIC)**  \n\n**Federal Housing Finance Agency (FHFA)**\n* [Advisory Bulletin AB 2013-07 Model Risk Management Guidance](https://www.fhfa.gov/sites/default/files/2023-03/ab_2013-07_model_risk_management_guidance.pdf)\n\n**Federal Reserve**\n* [Supervisory Guidance on Model Risk Management](https://www.federalreserve.gov/supervisionreg/srletters/sr1107a1.pdf)\n\n**Federal Trade Commission (FTC)**\n* [Business Blog](https://www.ftc.gov/business-guidance/blog)\n  * [2021-01-11 Facing the facts about facial recognition](https://www.ftc.gov/business-guidance/blog/2021/01/facing-facts-about-facial-recognition)  \n  * [2022-07-11 Location, health, and other sensitive information: FTC committed to fully enforcing the law against illegal use and sharing of highly sensitive data](https://www.ftc.gov/business-guidance/blog/2022/07/location-health-and-other-sensitive-information-ftc-committed-fully-enforcing-law-against-illegal)\n  * [2023-07-25 Protecting the privacy of health information: A baker’s dozen takeaways from FTC cases](https://www.ftc.gov/business-guidance/blog/2023/07/protecting-privacy-health-information-bakers-dozen-takeaways-ftc-cases)\n  * [2023-08-16 Can’t lose what you never had: Claims about digital ownership and creation in the age of generative AI](https://www.ftc.gov/business-guidance/blog/2023/08/cant-lose-what-you-never-had-claims-about-digital-ownership-creation-age-generative-ai)\n  * [2023-08-22 For business opportunity sellers, FTC says “AI” stands for “allegedly inaccurate”](https://www.ftc.gov/business-guidance/blog/2023/08/business-opportunity-sellers-ftc-says-ai-stands-allegedly-inaccurate)\n  * [2023-09-15 Updated FTC-HHS publication outlines privacy and security laws and rules that impact consumer health data](https://www.ftc.gov/business-guidance/blog/2023/09/updated-ftc-hhs-publication-outlines-privacy-security-laws-rules-impact-consumer-health-data)\n  * [2023-09-18 Companies warned about consequences of loose use of consumers’ confidential data](https://www.ftc.gov/business-guidance/blog/2023/09/companies-warned-about-consequences-loose-use-consumers-confidential-data)\n  * [2023-09-27 Could PrivacyCon 2024 be the place to present your research on AI, privacy, or surveillance?](https://www.ftc.gov/business-guidance/blog/2023/09/could-privacycon-2024-be-place-present-your-research-ai-privacy-or-surveillance)\n  * [2022-05-20 Security Beyond Prevention: The Importance of Effective Breach Disclosures](https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2022/05/security-beyond-prevention-importance-effective-breach-disclosures)\n  * [2023-02-01 Security Principles: Addressing underlying causes of risk in complex systems](https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2023/02/security-principles-addressing-underlying-causes-risk-complex-systems)\n  * [2023-06-29 Generative AI Raises Competition Concerns](https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2023/06/generative-ai-raises-competition-concerns)\n  * [2023-12-19 Coming face to face with Rite Aid’s allegedly unfair use of facial recognition technology](https://www.ftc.gov/business-guidance/blog/2023/12/coming-face-face-rite-aids-allegedly-unfair-use-facial-recognition-technology)\n* [Children's Online Privacy Protection Rule](https://www.ftc.gov/legal-library/browse/rules/childrens-online-privacy-protection-rule-coppa) | (\"COPPA\")\n* [Privacy Policy](https://www.ftc.gov/policy-notices/privacy-policy)\n\n**Food and Drug Administration**\n* [Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations](https://www.fda.gov/media/184856/download) | Draft Guidance for Industry and FDA Staff, January 7, 2025\n* [Artificial Intelligence/Machine Learning-Based: Software as a Medical Device Action Plan](https://www.fda.gov/media/145022/download) | SaMD, updated January 2021\n* [Software as a Medical Device guidance](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/software-medical-device-samd-clinical-evaluation) | SaMD guidance, December 8, 2017\n\n**General Services Administration**\n* [AI Guide for Government](https://coe.gsa.gov/coe/ai-guide-for-government/introduction/)\n\n**Government Accountability Office (GAO)**\n* [Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities, GAO-21-519SP](https://www.gao.gov/products/gao-21-519sp) | June 2021\n  * [Highlights of GAO-21-519SP](https://www.gao.gov/assets/gao-21-519sp-highlights.pdf)\n* [Artificial Intelligence: Generative AI Use and Management at Federal Agencies](https://www.gao.gov/assets/gao-25-107653.pdf) | July 2025\n* [Artificial Intelligence: Use and Oversight in Financial Services GAO-25-107197](https://www.gao.gov/assets/gao-25-107197.pdf) | May 19, 2025\n* [Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products Draft Guidance](https://www.fda.gov/media/184830/download) | January 2025\n* [Fraud and Improper Payments: Data Quality and a Skilled Workforce Are Essential for Unlocking the Benefits of Artificial Intelligence](https://www.gao.gov/assets/gao-25-108412.pdf)\n* [Generative AI's Environmental and Human Effects](https://www.gao.gov/assets/gao-25-107172.pdf) | Technology Assessment, Artificial Intelligence, April 2025\n* [Veteran Suicide: VA Efforts to Identify Veterans at Risk through Analysis of Health Record Information](https://www.gao.gov/assets/gao-22-105165.pdf)\n\n**NASA**\n* [Examining Proposed Uses of LLMs to Produce or Assess Assurance Arguments](https://ntrs.nasa.gov/api/citations/20250001849/downloads/NASA-TM-20250001849.pdf) | NASA/TM–20250001849, March 2025\n* [NASA Framework for the Ethical Use of Artificial Intelligence](https://ntrs.nasa.gov/api/citations/20210012886/downloads/NASA-TM-20210012886.pdf) | NASA/TM-20210012886, April 2021\n\n**National Archives**\n* [Potential Labor Market Impacts of Artificial Intelligence: An Empirical Analysis](https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/07/Potential-Labor-Market-Impacts-of-Artificial-Intelligence-An-Empirical-Analysis-July-2024.pdf)\n\n**National Association of Attorneys General**\n* [Letter to Congress Opposing AI Preemption Amendment](https://www.scag.gov/media/opvgxagq/2025-05-15-letter-to-congress-re-proposed-ai-preemption-_final.pdf) | Letter by state attorneys general opposing federal preemption of state AI regulation, May 16, 2025\n\n**National Endowment for the Humanities**\n* [Policy on the Use of Artificial Intelligence for NEH Grant Proposals](https://www.neh.gov/sites/default/files/2024-10/NEH.AI_.Policy-10.23.24.pdf) | October 23, 2024\n\n**National Security Agency (NSA)**\n* [Central Security Service, Artificial Intelligence Security Center](https://www.nsa.gov/AISC/)\n\n**National Security Commission on Artificial Intelligence**  \n* [Final Report](https://assets.foleon.com/eu-central-1/de-uploads-7e3kk3/48187/nscai_full_report_digital.04d6b124173c.pdf)\n\n**Office of the Comptroller of the Currency (OCC)**  \n* [2021 Model Risk Management Handbook](https://www.occ.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/index-model-risk-management.html)\n* [AI in Financial Services Remarks at NFHA Responsible AI Symposium](https://occ.gov/news-issuances/speeches/2025/pub-speech-2025-38.pdf) | Rodney E. Hood, April 29, 2025\n\n**Office of the Director of National Intelligence (ODNI)**\n* [The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines](https://www.dni.gov/files/ODNI/documents/AIM-Strategy.pdf)\n* [Artificial Intelligence Ethics Framework for the Intelligence Community v 1.0 as of June 2020](https://www.dni.gov/files/ODNI/documents/AI_Ethics_Framework_for_the_Intelligence_Community_10.pdf)\n* [Principles of Artificial Intelligence Ethics for the Intelligence Community](https://www.intel.gov/principles-of-artificial-intelligence-ethics-for-the-intelligence-community)\n* [Annual Threat Assessment of the U.S. Intelligence Community](https://www.dni.gov/files/ODNI/documents/assessments/ATA-2025-Unclassified-Report.pdf) | March 2025\n\n**Office of Management and Budget (OMB)**\n* [M-25-21 Memorandum for the Heads of Executive Departments and Agencies - Accelerating Federal Use of AI through Innovation, Governance, and Public Trust](https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf) | April 3, 2025\n* [M-25-22 Memorandum for the Heads of Executive Departments and Agencies - Driving Efficient Acquisition of Artificial Intelligence in Government](https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-22-Driving-Efficient-Acquisition-of-Artificial-Intelligence-in-Government.pdf) | April 3, 2025\n\n**Office of Personnel Management**\n* [The Artificial Intelligence Classification Policy and Talent Acquisition Guidance - The AI in Government Act of 2020](https://chcoc.gov/sites/default/files/The%20Artificial%20Intelligence%20Classification%20Policy%20and%20Talent%20Acquisition%20Guidance%20-%20The%20AI%20in%20Government%20Act%20of%202020.pdf) | April 29, 2024\n\n**Securities and Exchange Commission (SEC)**  \n* [Investor Advisory Committee Meeting Agenda for Thursday](https://www.sec.gov/about/advisory-committees/investor-advisory-committee/iac030625-agenda) | March 6, 2025\n* [SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence](https://www.sec.gov/news/press-release/2024-36)\n\n**Social Security Administration (SSA)**  \n* [Compliance Plan for OMB Memoranda M-24-10](https://www.ssa.gov/ai/policy/SSA%20M-24-10%20Compliance%20Plan.pdf) | September 2024\n\n**United States Patent and Trademark Office (USPTO)**\n* [Artificial Intelligence Strategy](https://www.uspto.gov/sites/default/files/documents/uspto-ai-strategy.pdf) | January 2025\n* [Public Views on Artificial Intelligence and Intellectual Property Policy](https://www.uspto.gov/sites/default/files/documents/USPTO_AI-Report_2020-10-07.pdf)\n\n**United States Congress, House of Representatives**\n* [Bipartisan House Task Force Report on Artificial Intelligence](https://republicans-science.house.gov/_cache/files/a/a/aa2ee12f-8f0c-46a3-8ff8-8e4215d6a72b/E4AF21104CB138F3127D8FF7EA71A393.ai-task-force-report-final.pdf) | 118th Congress, December 2024\n* [Letter to Inflection AI re: AI Censorship](https://judiciary.house.gov/sites/evo-subsites/republicans-judiciary.house.gov/files/evo-media-document/2025-03-13-jdj-to-inflection-ai-white-re-ai-censorship-1%29.pdf) | U.S. House Judiciary Committee, March 13, 2025\n\n**United States Congress, Senate**\n* [Decoupling America’s Artificial Intelligence Capabilities from China Act](https://www.hawley.senate.gov/wp-content/uploads/2025/01/Hawley-Decoupling-Americas-Artificial-Intelligence-Capabilities-from-China-Act.pdf) | U.S. Senate, 119th Congress, introduced by Senator Josh Hawley, January 2025\n* [Driving U.S. Innovation in Artificial Intelligence: A Roadmap for Artificial Intelligence Policy in the United States Senate](https://www.schumer.senate.gov/imo/media/doc/Roadmap_Electronic1.32pm.pdf) | The Bipartisan Senate AI Working Group, May 2024\n* [Letter to DOJ Re FARA AI Violation](https://www.commerce.senate.gov/services/files/55267EFF-11A8-4BD6-BE1E-61452A3C48E3) | Senator Ted Cruz to Attorney General Merrick Garland, Committee on Commerce, Science, and Transportation, 2024.11.21\n* [Letter to Sundar Pichai concerning Google's decision to reverse its previous safety and ethical commitments on its development of AI products](https://www.markey.senate.gov/imo/media/doc/letter_to_google_on_ai_principles_revisions2.pdf) | Letter from Senators Edward J. Markey, Jeffrey A. Merkley, and Peter Welch, February 19, 2025\n\n**United States Web Design System (USWDS)**\n* [Design principles](https://designsystem.digital.gov/design-principles/)\n\n#### United States (State Governments)\n\n**Alabama**\n* [Artificial Intelligence Governance Policy AI-GV-P1](https://oit.alabama.gov/wp-content/uploads/2025/01/Artificial-Intelligence-Governance-Policy.pdf) | State of Alabama, Office of Information Technology, Version 1, Effective January 31, 2025\n* [Generative AI Task Force Final Report](https://governor.alabama.gov/assets/2025/03/GenAI-TaskForce-Report_Final_20250321.pdf) | State of Alabama, Governor's Task Force on Generative Artificial Intelligence: Providing for the Responsible and Productive Use of Generative Artificial Intelligence in State Government, March 2025\n\n**California**\n* [California Consumer Privacy Act](https://oag.ca.gov/privacy/ccpa) | (CCPA)\n* California Department of Justice\n  * [How to Read a Privacy Policy](https://www.oag.ca.gov/privacy/facts/online-privacy/privacy-policy)\n  * [Office of the Attorney General, California Attorney General's Legal Advisory on the Application of Existing California Laws to Artificial Intelligence](https://oag.ca.gov/system/files/attachments/press-docs/Legal%20Advisory%20-%20Application%20of%20Existing%20CA%20Laws%20to%20Artificial%20Intelligence.pdf)\n* [California Department of Technology, GenAI Executive Order](https://cdt.ca.gov/technology-innovation/artificial-intelligence-community/genai-executive-order/)\n* [California Privacy Protection Agency, Draft Risk Assessment and Automated Decisionmaking Technology Regulations](https://cppa.ca.gov/meetings/materials/20240308_item4_draft_risk.pdf) | CPPA, March 2024\n* [The California Report on Frontier AI Policy](https://www.gov.ca.gov/wp-content/uploads/2025/06/June-17-2025-%E2%80%93-The-California-Report-on-Frontier-AI-Policy.pdf) | Joint California Policy Working Group on AI Frontier Models, June 17, 2025\n* [Office of the Attorney General of California, California Privacy Rights Act](https://www.oag.ca.gov/system/files/initiatives/pdfs/19-0021A1%20%28Consumer%20Privacy%20-%20Version%203%29_1.pdf) | (CPRA)\n* [Sonoma County Administrative Policy 9-6 Information Technology Artificial Intelligence Policy](https://sonomacounty.ca.gov/Main%20County%20Site/Administrative%20Support%20%26%20Fiscal%20Services/HR/Employee%20Resources/Administrative%20Policy%20Manual/9-6%20AI%20Policy/AI%20Policy.pdf) | September 10, 2024\n\n**Colorado**\n* [Report and Recommendations: Artificial Intelligence Impact Task Force](https://leg.colorado.gov/sites/default/files/images/report_and_recommendations_5.pdf) | Colorado Legislative Council Staff, February 2025\n\n**Connecticut**\n* [State of Connecticut Judicial Branch JBAPPM Policy 1013 Artificial Intelligence Responsible Use Framework, Meaningful Guardrails + Workforce Empowerment and Education + Purposeful Use = Responsible AI Innovation](https://www.jud.ct.gov/faq/CTJBResponsibleAIPolicyFramework2.1.24.pdf) | Version 1.0, February 1, 2024\n* [State of Connecticut Policy AI-01 AI Responsible Use Framework, Meaningful Guardrails + Workforce Empowerment and Education + Purposeful Use = Responsible AI Innovation](https://portal.ct.gov/-/media/OPM/Fin-General/Policies/CT-Responsible-AI-Policy-Framework-Final-02012024.pdf) | Version 1.0, February 1, 2024\n\n**Florida**\n* [Provenance of Digital Content Florida HB 369 Bill Analysis](https://www.flsenate.gov/Session/Bill/2025/369/Analyses/h0369e.COM.PDF) | Florida House of Representatives, April 2025\n* [Report on Miami-Dade County's Policy on Artificial Intelligence–Directive No. 231203](https://documents.miamidade.gov/mayor/memos/03.22.24-Report-on-Miami-Dade-Countys-Policy-on-Artificial-Intelligence-Directive-No-231203.pdf) | Miami-Dade County, March 22, 2024\n* [Second Report on Miami-Dade County's Policy on Artificial Intelligence Directive No. 231203](https://www.miamidade.gov/technology/library/artificial-intelligence-report-2025.pdf) | Miami-Dade County, April 8, 2025\n\n**Illinois**\n* [Illinois Supreme Court Policy on Artificial Intelligence](https://ilcourtsaudio.blob.core.windows.net/antilles-resources/resources/e43964ab-8874-4b7a-be4e-63af019cb6f7/Illinois%20Supreme%20Court%20AI%20Policy.pdf) | Effective January 1, 2025\n\n**Indiana**\n* [State of Indiana Artificial Intelligence](https://www.in.gov/mph/cdo/files/State-of-Indiana-Artificial-Intelligence-Policy.pdf) | Version 1.1, December 2024\n\n**Kentucky**\n* [080.101 AI/Gen AI Policy Version 1.1](https://www.chfs.ky.gov/agencies/os/oats/polstand/080101AIGen%20AI.pdf) | Cabinet for Health and Family Services, February 27, 2025\n* [Artificial Intelligence Guidance Brief 2024](https://www.education.ky.gov/districts/tech/Documents/AI%20Guidance%20Brief.pdf) | Kentucky Department of Education\n* [Research Report No. 491 Executive Branch Use of Artificial Intelligence Technology](https://apps.legislature.ky.gov/lrc/publications/ResearchReports/RR491.pdf) | Legislative Research Commission\n\n**Maine**\n* [Generative Artificial Intelligence Policy](https://www.maine.gov/oit/sites/maine.gov.oit/files/inline-files/GenAIPolicy.pdf) | Maine State Government, Department of Administrative and Financial Services, Office of Information Technology (OIT), issued July 19, 2024, revised February 28, 2025\n\n**Massachusetts**\n* [Enterprise Use and Development of Generative Artificial Intelligence Policy](https://www.mass.gov/doc/enterprise-use-and-development-of-generative-artificial-intelligence-policy/download) | Executive Office of Technology Services and Security (EOTSS), Enterprise Privacy Office, GenAI Policy, AI.001, January 31, 2025\n\n**Mississippi**\n* [Mississippi Department of Education, Artificial Intelligence Guidance for K-12 Classrooms](https://www.mdek12.org/sites/default/files/Offices/MDE/OTSS/DL/ai_guidance_final.pdf)\n\n**Nebraska**\n* [Nebraska Information Technology Commission 8-609 Artificial intelligence policy](https://nitc.nebraska.gov/standards/8-609.pdf) | November 8, 2024\n\n**New Jersey**\n* [Legal Practice Preliminary Guidelines on the Use of Artificial Intelligence by New Jersey Lawyers](https://www.njcourts.gov/sites/default/files/notices/2024/01/n240125a.pdf)\n\n**New York**\n* [Acceptable Use of Artificial Intelligence Technologies](https://its.ny.gov/system/files/documents/2025/04/nys-p24-001-acceptable-use-of-artificial-intelligence-technologies.pdf) | Office of Information Technology Services, March 11, 2025\n* [The New York City Artificial Intelligence Action Plan](https://www.nyc.gov/assets/oti/downloads/pdf/reports/artificial-intelligence-action-plan.pdf) | October 2023\n* [New York City Automated Decision Systems Task Force Report](https://www.nyc.gov/assets/adstaskforce/downloads/pdf/ADS-Report-11192019.pdf) | November 2019\n* [New York State Emerging Technology Advisory Board: Recommendations for making NY a leader in responsible AI](https://filecache.mediaroom.com/mr5mr_ibmnewsroom/198517/IBM-ETAB-Report-white-paper-DIGITAL-20241212%5B30%5D.pdf)\n* [New York State Artificial Intelligence Governance](https://www.osc.ny.gov/files/state-agencies/audits/pdf/sga-2025-23s50.pdf) | Office of the New York State Comptroller, Report 2023-S-50, April 2025\n* [Use of External Consumer Data and Information Sources in Underwriting for Life Insurance](https://www.dfs.ny.gov/industry_guidance/circular_letters/cl2019_01)\n\n**North Carolina**\n* [AI Accelerator](https://it.nc.gov/resources/artificial-intelligence/ai-accelerator) | Department of Information Technology\n* [North Carolina State Government Responsible Use of Artificial Intelligence Framework](https://it.nc.gov/documents/nc-state-government-responsible-use-artificial-intelligence-framework/download?attachment) | August 2024\n\n**South Carolina**\n* [South Carolina State Agencies Artificial Intelligence Strategy](https://admin.sc.gov/sites/admin/files/Documents/OED/Final%20SC%20AI%20Strategy.pdf) | June 2024\n\n**North Dakota**\n* [State of North Dakota Artificial Intelligence Policy](https://www.ndit.nd.gov/sites/www/files/documents/Policies/artificial_intelligence_policy_2024.pdf)\n\n**Pennsylvania**\n* [Artificial Intelligence Policy](https://www.pa.gov/content/dam/copapwp-pagov/en/oa/documents/policies/it-policies/artificial%20intelligence%20policy.pdf) | Office of Administration, March 11, 2025\n* [Lessons from Pennsyklvania's Generative AI Pilot with ChatGPT](https://www.pa.gov/content/dam/copapwp-pagov/en/oa/documents/programs/information-technology/documents/openai-pilot-report-2025.pdf) | March 2025\n\n**Tennessee**\n* [Artificial Intelligence and Generative AI Policy ISM 20](https://www.nashville.gov/sites/default/files/2024-04/ISM-20-Artificial-Intelligence-and-Generative-Artificial-Intelligence-Use.pdf?ct=1713207273) | Metropolitan Government of Nashville and Davidson County, Department of Information Technology Services, Version 1.0, April 15, 2024\n* [Enterprise Artificial Intelligence policy 200-POL-007](https://www.tn.gov/content/dam/tn/finance/artificial-intelligence/Enterprise_Artificial_Intelligence_Policy.pdf) | Department of Finance and Administration, Strategic Technology Solutions, Version 1.0, October 30, 2024\n\n**Texas**\n* [Artificial Intelligence Strategic Plan Fiscal Years 2025-2027](https://www.txdot.gov/content/dam/docs/str/ai-strategic-plan-09-20-2024.pdf) | Texas Department of Transportation\n* [Federal Reserve Bank of Dallas, Regulation B, Equal Credit Opportunity, Credit Scoring Interpretations: Withdrawal of Proposed Business Credit Amendments](https://fraser.stlouisfed.org/files/docs/historical/frbdal/circulars/frbdallas_circ_19820603_no82-063.pdf) | June 3, 1982\n\n**Utah**\n* [Artificial Intelligence Framework for Utah P-12 Education](https://www.utah.gov/pmn/files/1116147.pdf) | March 2024\n* [Questions from the Commission on Protecting Privacy and Preventing Discrimination](https://auditor.utah.gov/wp-content/uploads/sites/6/2021/02/Office-of-the-State-Auditor-Questions-to-help-Procuring-Agencies-_-Entities-with-Software-Procurement-Feb-1-2021-Final.pdf)\n\n**Virginia**\n* [Policy Standards for the Utilization of Artificial Intelligence by the Commonwealth of Virginia](https://www.vita.virginia.gov/media/vitavirginiagov/it-governance/ea/pdf/Utilization-of-Artificial-Intelligence-by-COV-Policy-Standard.pdf)\n\n**Washington**\n* [City of Seattle Generative Artificial Intelligence Policy POL-209](https://seattle.gov/documents/Departments/SeattleIT/City-of-Seattle-Generative-Artificial-Intelligence-Policy.pdf)\n* [Washington Technology Solutions Reports & Documents](https://watech.wa.gov/about/reports-documents)\n  * [Guidelines for Deployment of Generative AI](https://watech.wa.gov/sites/default/files/2024-12/Equity%20Analysis%20Guidelines%20for%20Deployment%20of%20Generative%20AI-WaTech_2024.pdf) | December 2024\n  * [Implementing risk assessments for high-risk AI systems](https://watech.wa.gov/sites/default/files/2025-01/EO%2024-01%20Risk%20Guidance_Final.pdf) | December 2024\n  * [Initial procurement guidelines for public sector procurement, deployment, and monitoring of Generative AI Technology](https://watech.wa.gov/sites/default/files/2024-11/Initial%20Procurement%20Guidelines%20for%20GenAI%20Final.pdf) | September 2024\n  * [Interim Guidelines for Purposeful and Responsible Use of Generative Artificial Intelligence](https://watech.wa.gov/sites/default/files/2023-09/State%2520Agency%2520Generative%2520AI%2520Guidelines%25208-7-23%2520.pdf) | August 8, 2023\n  * [Office of Privacy and Data Protection Performance Report](https://watech.wa.gov/sites/default/files/2024-12/OPDP%202024%20Performance%20Report%20Final%2012-1-24.pdf) | December 1, 2024\n  * [Responsible AI in the Public Sector: How the Washington State Government Uses & Governs Artificial Intelligence](https://watech.wa.gov/sites/default/files/2025-01/Responsible%20AI%20in%20the%20Public%20Sector%20-%20WaTech%20%20UC%20Berkeley%20Report%20-%20Final_.pdf) | January 31, 2025\n  * [State of Washington Generative Artificial Intelligence Report](https://watech.wa.gov/sites/default/files/2024-10/WA_State_GenAIReport_FINAL.pdf) | September 2024\n\n**Wyoming**\n* Wyoming Department of Education (WDE)\n * [AI Guidance Resources](https://wde.instructure.com/courses/826)\n * [Guidance for Wyoming School Districts on Developing Artificial Intelligence Use Policy](https://edu.wyoming.gov/wp-content/uploads/2024/06/Guidance-for-AI-Policy-Development.pdf)\n\n#### International and Multilateral Frameworks\n\n#### ASEAN\n\n* [ASEAN Guide on AI Governance and Ethics](https://asean.org/wp-content/uploads/2024/02/ASEAN-Guide-on-AI-Governance-and-Ethics_beautified_201223_v2.pdf)\n\n#### European Union Policies and Regulations\n\n#### Council of Europe\n\n* [Democracy and the Rule of Law](https://rm.coe.int/1680afae3c) | Council of Europe Framework Convention on Artificial Intelligence and Human Rights\n* [Discussion paper on Draft Recommendation on AI literacy](https://rm.coe.int/discussion-paper-on-draft-recommendation-on-ai-literacy/1680b5b6f2) | Wayne Holmes, February 25, 2025\n* [European Audiovisual Observatory, IRIS, AI and the audiovisual sector: navigating the current legal landscape](https://rm.coe.int/iris-2024-3-ia-legal-landscape/1680b1e999)\n* [Guidelines on the Responsible Implementation of Artificial Intelligence Systems in Journalism](https://rm.coe.int/cdmsi-2023-014-guidelines-on-the-responsible-implementation-of-artific/1680adb4c6)\n* [On the Use of Artificial Intelligence in the Framework of the Syrian War](https://www.genocideprevention.eu/files/On_the_Use_of_Artificial_Intelligence_in_the_framework_of_the_Syrian_War.pdf) | Budapest Centre for Mass Atrocities Prevention, July 2021\n* [Privacy and Data Protection Risks in Large Language Models](https://rm.coe.int/privacy-and-data-protection-risks-in-large-language-models-llms-v1-0/1680b631dd) | Consultative Committee of the Convention for the Protection of Individuals with Regard to Automatic Processing of Personal Data, Convention 108, June 17, 2025\n* [Recommendation CM/Rec-2020-1 of the Committee of Ministers to member States on the human rights impacts of algorithmic systems](https://search.coe.int/cm?i=09000016809e1154) | Adopted by the Committee of Ministers on 8 April 2020 at the 1373rd meeting of the Ministers’ Deputies\n* [The Framework Convention on Artificial Intelligence](https://www.coe.int/en/web/artificial-intelligence/the-framework-convention-on-artificial-intelligence)\n  * [Explanatory Report to the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law](https://rm.coe.int/1680afae67)\n\n#### European Commission and Parliament\n\n* [AI Act: Commission issues draft guidance and reporting template on serious AI incidents, and seeks stakeholders' feedback](https://digital-strategy.ec.europa.eu/en/consultations/ai-act-commission-issues-draft-guidance-and-reporting-template-serious-ai-incidents-and-seeks)\n* [AI-driven Innovation in Medical Imaging: Focus on Lung Cancer and Cardiovascular Diseases](https://publications.jrc.ec.europa.eu/repository/bitstream/JRC142224/JRC142224_01.pdf)\n* [Assessment List for Trustworthy Artificial Intelligence for self-assessment - Shaping Europe’s digital future - European Commission](https://ec.europa.eu/digital-single-market/en/news/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment) | (ALTAI)\n* [Addressing AI risks in the workplace: Workers and algorithms](https://tinyurl.com/38sxrjtk) | European Parliament\n* [Artificial Intelligence and Civil Liability: A European Perspective](https://www.europarl.europa.eu/RegData/etudes/STUD/2025/776426/IUST_STU%282025%29776426_EN.pdf) | European Parliament, Policy Department for Justice, Civil Liberties and Institutional Affairs, Directorate-General for Citizens' Rights, Justice and Institutional Affairs, July 2025\n* [Artificial intelligence and human rights: Using AI as a weapon of repression and its impact on human rights](https://www.europarl.europa.eu/RegData/etudes/IDAN/2024/754450/EXPO_IDA%282024%29754450_EN.pdf) | H. Akin Ünver, May 2024\n* [Civil liability regime for artificial intelligence](https://www.europarl.europa.eu/doceo/document/TA-9-2020-0276_EN.pdf)\n* European Commission\n  * [Analysis of the preliminary AI standardisation work plan in support of the AI Act](https://publications.jrc.ec.europa.eu/repository/handle/JRC132833)\n  * [A Framework to Categorise Modified General-Purpose AI Models as New Models Based on Behavioural Changes](https://publications.jrc.ec.europa.eu/repository/bitstream/JRC143257/JRC143257_01.pdf)\n  * [Communication from the Commission, Artificial Intelligence for Europe](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2018%3A237%3AFIN) | (4/25/2018)\n  * [Data Protection Certification Mechanisms: Study on Articles 42 and 43 of the Regulation 2016/679](https://commission.europa.eu/publications/study-data-protection-certification-mechanisms_en?prefLang=lv) | (EU)\n  * [Data quality and artificial intelligence - mitigating bias and error to protect fundamental rights](https://fra.europa.eu/sites/default/files/fra_uploads/fra-2019-data-quality-and-ai_en.pdf) | European Union Agency for Fundamental Rights\n  * [Data Union Strategy: Unlocking Data for AI](https://ec.europa.eu/newsroom/dae/redirection/document/121745)\n  * [Ethical guidelines on the use of artificial intelligence and data in teaching and learning for Educators](https://school-education.ec.europa.eu/system/files/2023-12/ethical_guidelines_on_the_use_of_artificial_intelligence-nc0722649enn_0.pdf)\n  * [Ethics By Design and Ethics of Use Approaches for Artificial Intelligence](https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ethics-by-design-and-ethics-of-use-approaches-for-artificial-intelligence_he_en.pdf) | Version 1.0, November 25, 2021\n  * [Ethics Guidelines for Trustworthy AI](https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf) | European Commission Independent High-Level Expert Group on Artificial Intelligence, April 8, 2019\n  * [European approach to artificial intelligence](https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence)\n  * [First Draft of the General-Purpose AI Code of Practice published, written by independent experts](https://ec.europa.eu/newsroom/dae/redirection/document/109946)\n  * [A Framework to Categorise Modified General-Purpose AI Models as New Models Based on Behavioural Changes](https://publications.jrc.ec.europa.eu/repository/bitstream/JRC143257/JRC143257_01.pdf)\n  * [Generative AI and the EUDPR. Orientations for ensuring data protection compliance when using Generative AI systems. Version 2](https://www.edps.europa.eu/system/files/2025-10/25-10_28_revised_genai_orientations_en.pdf) | European Data Protection Supervisor, October 28, 2025\n  * [Hiroshima Process International Guiding Principles for Advanced AI system](https://digital-strategy.ec.europa.eu/en/library/hiroshima-process-international-guiding-principles-advanced-ai-system)\n  * [Ethics Guidelines for Trustworthy AI](https://www.europarl.europa.eu/cmsdata/196377/AI%20HLEG_Ethics%20Guidelines%20for%20Trustworthy%20AI.pdf) | Independent High-Level Expert Group on Artificial Intelligence\n  * [Living Guidelines on the Responsible Use of Generative AI in Research](https://research-and-innovation.ec.europa.eu/document/download/2b6cf7e5-36ac-41cb-aab5-0d32050143dc_en?filename=ec_rtd_ai-guidelines.pdf) | (ERA Forum Stakeholders' document, First Version, March 2024)\n  * [Living repository to foster learning and exchange on AI literacy](https://digital-strategy.ec.europa.eu/en/library/living-repository-foster-learning-and-exchange-ai-literacy)\n  * [Living Repository of AI Literacy Practices](https://ec.europa.eu/newsroom/dae/redirection/document/112203) | v. 31.01.2025\n  * [Policy and Investment Recommendations for Trustworthy AI](https://www.europarl.europa.eu/cmsdata/196378/AI%20HLEG_Policy%20and%20Investment%20Recommendations.pdf) | Independent High-Level Expert Group on Artificial Intelligence\n  * [Procurement of AI Updated EU AI model contractual clauses](https://public-buyers-community.ec.europa.eu/communities/procurement-ai/resources/updated-eu-ai-model-contractual-clauses)\n  * [Work in the Digital Era: How Technology is Transforming Work and Occupations](https://publications.jrc.ec.europa.eu/repository/bitstream/JRC141451/JRC141451_01.pdf) | September 11, 2025\n* [Proposal for a directive on adapting non-contractual civil liability rules to artificial intelligence: Complementary impact assessment](https://tinyurl.com/3yvcp8pa)\n* [Proposal for a Regulation laying down harmonised rules on artificial intelligence](https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence) | (Artificial Intelligence Act)\n  * [Amendments adopted by the European Parliament on 14 June 2023 on the proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on artificial intelligence and amending certain Union legislative acts](https://www.europarl.europa.eu/doceo/document/TA-9-2023-0236_EN.html) |  (Artificial Intelligence Act)\n* [The Digital Services Act package](https://digital-strategy.ec.europa.eu/en/policies/digital-services-act-package) | (EU Digital Services Act and Digital Markets Act)\n* [The impact of the General Data Protection Regulation on artificial intelligence](https://tinyurl.com/ynf3m8zf) | (GDPR), European Parliament\n* [Roadmap for lawful and effective access to data for law enforcement](https://data.consilium.europa.eu/doc/document/ST-10806-2025-INIT/en/pdf) | June 24, 2025\n\n#### European Council\n\n* [Artificial intelligence act: Council and Parliament strike a deal on the first rules for AI in the world](https://www.consilium.europa.eu/en/press/press-releases/2023/12/09/artificial-intelligence-act-council-and-parliament-strike-a-deal-on-the-first-worldwide-rules-for-ai/)\n* [Council Conclusions on the Use of Artificial Intelligence in the Field of Justice](https://data.consilium.europa.eu/doc/document/ST-16933-2024-INIT/en/pdf) | December 16, 2024\n\n#### European Data Protection Authorities\n\n* [AI Auditing documents](https://www.edpb.europa.eu/our-work-tools/our-documents/support-pool-expert-projects/ai-auditing_en) | European Data Protection Board (EDPB)\n* [Artificial Intelligence and Algorithms in Risk Assessment: Addressing Bias, Discrimination and other Legal and Ethical Issues](https://www.ela.europa.eu/sites/default/files/2023-08/ELA-Handbook-AI-training.pdf) | European Labour Authority (ELA)\n* [Data Protection Authority of Belgium General Secretariat, Artificial Intelligence Systems and the GDPR: A Data Protection Perspective](https://www.autoriteprotectiondonnees.be/publications/artificial-intelligence-systems-and-the-gdpr---a-data-protection-perspective.pdf)\n* [First EDPS Orientations for EUIs using Generative AI](https://www.edps.europa.eu/data-protection/our-work/publications/guidelines/2024-06-03-first-edps-orientations-euis-using-generative-ai_en) | European Data Protection Supervisor\n* [Generative AI and the EUDPR. First EDPS Orientations for ensuring data protection compliance when using Generative AI systems](https://www.edps.europa.eu/system/files/2024-06/24-06-03_genai_orientations_en.pdf) | European Data Protection Supervisor, June 3, 2024\n* [Opinion 28/2024 on certain data protection aspects related to the processing of personal data in the context of AI models](https://www.edpb.europa.eu/system/files/2024-12/edpb_opinion_202428_ai-models_en.pdf) | European Data Protection Board (EDPB)\n* [Training curriculum on AI and data protection: Fundamentals of Secure AI Systems with Personal Data](https://www.edpb.europa.eu/system/files/2025-06/spe-training-on-ai-and-data-protection-technical_en.pdf)\n\n#### European Union entities (various)\n\n* [Analysis of EU AI Office stakeholder consultations: defining AI systems and prohibited applications](https://ec.europa.eu/newsroom/dae/redirection/document/115454) | Centre for European Policy Studies, May 2025\n  * [Multi-Stakeholder Consultation for Commission Guidelines on the Application of the Definition of an AI System and the Prohibited AI Practices Established in the AI Act](https://ec.europa.eu/newsroom/dae/redirection/document/115453)\n* [The changing DNA of serious and organised crime](https://www.europol.europa.eu/cms/sites/default/files/documents/EU-SOCTA-2025.pdf) | Europol 2025\n* [Guiding Principles for Automated Decision-Making in the EU](https://www.europeanlawinstitute.eu/fileadmin/user_upload/p_eli/Publications/ELI_Innovation_Paper_on_Guiding_Principles_for_ADM_in_the_EU.pdf) | European Law Institute (ELI), 2022\n* [Trustworthiness for AI in Defence: Developing Responsible, Ethical, and Trustworthy AI Systems for European Defence](https://eda.europa.eu/docs/default-source/brochures/taid-white-paper-final-09052025.pdf) | European Defence Agency, May 9, 2025\n\n#### OECD\n* [AI, data governance and privacy: Synergies and areas of international co-operation](https://www.oecd.org/en/publications/ai-data-governance-and-privacy_2476b1a4-en.html) | June 26, 2024\n* [Algorithm Impact Assessment Toolkit](https://oecd.ai/en/catalogue/tools/algorithm-impact-assessment-toolkit)\n* [OECD.AI Catalogue of Tools & Metrics for Trustworthy AI, Anekanta AI, Responsible AI Governance Framework for boards](https://oecd.ai/en/catalogue/tools/responsible-ai-governance-framework-for-boards)\n* [OECD Artificial Intelligence Papers](https://www.oecd-ilibrary.org/science-and-technology/oecd-artificial-intelligence-papers_dee339a8-en)\n  * [No. 1, September 18, 2023, Initial policy considerations for generative artificial intelligence](https://www.oecd-ilibrary.org/deliver/fae2d1e6-en.pdf?itemId=%2Fcontent%2Fpaper%2Ffae2d1e6-en&mimeType=pdf)\n  * [No. 2, October 17, 2023, Emerging trends in AI skill demand across 14 OECD countries](https://www.oecd-ilibrary.org/deliver/7c691b9a-en.pdf?itemId=%2Fcontent%2Fpaper%2F7c691b9a-en&mimeType=pdf)\n  * [No. 3, October 27, 2023, The state of implementation of the OECD AI Principles four years on](https://www.oecd-ilibrary.org/deliver/835641c9-en.pdf?itemId=%2Fcontent%2Fpaper%2F835641c9-en&mimeType=pdf)\n  * [No. 4, October 27, 2023, Stocktaking for the development of an AI incident definition](https://www.oecd-ilibrary.org/deliver/c323ac71-en.pdf?itemId=%2Fcontent%2Fpaper%2Fc323ac71-en&mimeType=pdf)\n  * [No. 5, November 7, 2023, Common guideposts to promote interoperability in AI risk management](https://www.oecd-ilibrary.org/deliver/ba602d18-en.pdf?itemId=%2Fcontent%2Fpaper%2Fba602d18-en&mimeType=pdf)\n  * [No. 6, November 13, 2023, What technologies are at the core of AI?](https://www.oecd-ilibrary.org/deliver/32406765-en.pdf?itemId=%2Fcontent%2Fpaper%2F32406765-en&mimeType=pdf)\n  * [No. 7, November 24, 2023, Using AI to support people with disability in the labour market](https://www.oecd-ilibrary.org/deliver/008b32b7-en.pdf?itemId=%2Fcontent%2Fpaper%2F008b32b7-en&mimeType=pdf)\n  * [No. 8, March 5, 2024, Explanatory memorandum on the updated OECD definition of an AI system](https://www.oecd-ilibrary.org/deliver/623da898-en.pdf?itemId=%2Fcontent%2Fpaper%2F623da898-en&mimeType=pdf)\n  * [No. 9, December 15, 2023, Generative artificial intelligence in finance](https://www.oecd-ilibrary.org/deliver/ac7149cc-en.pdf?itemId=%2Fcontent%2Fpaper%2Fac7149cc-en&mimeType=pdf)\n  * [No. 10, January 19, 2024, Collective action for responsible AI in health](https://www.oecd-ilibrary.org/deliver/f2050177-en.pdf?itemId=%2Fcontent%2Fpaper%2Ff2050177-en&mimeType=pdf)\n  * [No. 11, March 15, 2024, Using AI in the workplace](https://www.oecd-ilibrary.org/deliver/73d417f9-en.pdf?itemId=%2Fcontent%2Fpaper%2F73d417f9-en&mimeType=pdf)\n  * [No. 12, March 22, 2024, Generative AI for anti-corruption and integrity in government](https://www.oecd-ilibrary.org/deliver/657a185a-en.pdf?itemId=%2Fcontent%2Fpaper%2F657a185a-en&mimeType=pdf)\n  * [No. 13, April 10, 2024, Artificial intelligence and wage inequality](https://www.oecd-ilibrary.org/deliver/bf98a45c-en.pdf?itemId=%2Fcontent%2Fpaper%2Fbf98a45c-en&mimeType=pdf)\n  * [No. 14, April 10, 2024, Artificial intelligence and the changing demand for skills in the labour market](https://www.oecd-ilibrary.org/deliver/88684e36-en.pdf?itemId=%2Fcontent%2Fpaper%2F88684e36-en&mimeType=pdf)\n  * [No. 15, April 16, 2024, The impact of Artificial Intelligence on productivity, distribution and growth](https://www.oecd-ilibrary.org/deliver/8d900037-en.pdf?itemId=%2Fcontent%2Fpaper%2F8d900037-en&mimeType=pdf)\n  * [No. 16, May 6, 2024, Defining AI incidents and related terms](https://www.oecd-ilibrary.org/deliver/d1a8d965-en.pdf?itemId=%2Fcontent%2Fpaper%2Fd1a8d965-en&mimeType=pdf)\n  * [No. 17, May 30, 2024, Artificial intelligence and the changing demand for skills in Canada](https://www.oecd-ilibrary.org/deliver/1b20cdb6-en.pdf?itemId=%2Fcontent%2Fpaper%2F1b20cdb6-en&mimeType=pdf)\n  * [No. 18, May 24, 2024, Artificial intelligence, data and competition](https://www.oecd-ilibrary.org/deliver/e7e88884-en.pdf?itemId=%2Fcontent%2Fpaper%2Fe7e88884-en&mimeType=pdf)\n  * [No. 19, June 13, 2024, A new dawn for public employment services](https://www.oecd-ilibrary.org/deliver/5dc3eb8e-en.pdf?itemId=%2Fcontent%2Fpaper%2F5dc3eb8e-en&mimeType=pdf)\n  * [No. 20, June 13, 2024, Governing with Artificial Intelligence](https://www.oecd-ilibrary.org/deliver/26324bc2-en.pdf?itemId=%2Fcontent%2Fpaper%2F26324bc2-en&mimeType=pdf)\n  * [No. 21, June 24, 2024, Using AI to manage minimum income benefits and unemployment assistance](https://www.oecd-ilibrary.org/deliver/718c93a1-en.pdf?itemId=%2Fcontent%2Fpaper%2F718c93a1-en&mimeType=pdf)\n  * [No. 22, June 26, 2024, AI, data governance and privacy](https://www.oecd-ilibrary.org/deliver/2476b1a4-en.pdf?itemId=%2Fcontent%2Fpaper%2F2476b1a4-en&mimeType=pdf)\n  * [No. 23, August 14, 2024, The potential impact of Artificial Intelligence on equity and inclusion in education](https://www.oecd-ilibrary.org/deliver/15df715b-en.pdf?itemId=%2Fcontent%2Fpaper%2F15df715b-en&mimeType=pdf)\n  * [No. 24, September 5, 2024, Regulatory approaches to Artificial Intelligence in finance](https://www.oecd-ilibrary.org/deliver/f1498c02-en.pdf?itemId=%2Fcontent%2Fpaper%2Ff1498c02-en&mimeType=pdf)\n  * [No. 25, September 5, 2024, Measuring the demand for AI skills in the United Kingdom](https://www.oecd-ilibrary.org/deliver/1d6474ef-en.pdf?itemId=%2Fcontent%2Fpaper%2F1d6474ef-en&mimeType=pdf)\n  * [No. 26, October 31, 2024, Who will be the workers most affected by AI?](https://www.oecd-ilibrary.org/deliver/14dc6f89-en.pdf?itemId=%2Fcontent%2Fpaper%2F14dc6f89-en&mimeType=pdf)\n  * [No. 27, November 14, 2024, Assessing potential future artificial intelligence risks, benefits and policy imperatives](https://www.oecd-ilibrary.org/deliver/3f4e3dfb-en.pdf?itemId=%2Fcontent%2Fpaper%2F3f4e3dfb-en&mimeType=pdf)\n  * [No. 28, November 20, 2024, Artificial Intelligence and the health workforce](https://www.oecd-ilibrary.org/deliver/9a31d8af-en.pdf?itemId=%2Fcontent%2Fpaper%2F9a31d8af-en&mimeType=pdf)\n  * [No. 29, November 22, 2024, Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence](https://www.oecd-ilibrary.org/deliver/b524a072-en.pdf?itemId=%2Fcontent%2Fpaper%2Fb524a072-en&mimeType=pdf)\n  * [No. 30, December 12, 2024, A Sectoral Taxonomy of AI Intensity](https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/12/a-sectoral-taxonomy-of-ai-intensity_c2baae71/1f6377b5-en.pdf)\n  * [No. 31, February 6, 2025, Algorithmic Management in the Workplace: New Evidence from an OECD Employer Survey](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/algorithmic-management-in-the-workplace_3c84ed6d/287c13c4-en.pdf)\n  * [No. 32, February 7, 2025, Steering AI's Future: Strategies for Anticipatory Governance](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/steering-ai-s-future_70e4a856/5480ff0a-en.pdf)\n  * [No. 33, February 9, 2025, Intellectual Property Issues in Artificial Intelligence Trained on Scraped Data](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/intellectual-property-issues-in-artificial-intelligence-trained-on-scraped-data_a07f010b/d5241a23-en.pdf)\n  * [No. 34, February 28, 2025, Towards a Common Reporting Framework for AI Incidents](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/towards-a-common-reporting-framework-for-ai-incidents_8c488fdb/f326d4ac-en.pdf)\n  * [No. 35, February 28, 2025, AI Skills and Capabilities in Canada](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/ai-skills-and-capabilities-in-canada_09294563/87f76682-en.pdf)\n  * [No. 36, May 28, 2025, Digital and AI skills in health occupations: What do we know about new demand?](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/05/digital-and-ai-skills-in-health-occupations_f428e5a9/5fbd42ab-en.pdf)\n  * [No. 37, June 17, 2025, Sharing trustworthy AI models with privacy-enhancing technologies](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/sharing-trustworthy-ai-models-with-privacy-enhancing-technologies_5df6fd05/a266160b-en.pdf)\n  * [No. 38, June 17, 2025, Developments in Artificial Intelligence markets: New indicators based on model characteristics, prices and providers](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/developments-in-artificial-intelligence-markets-new-indicators-based-on-model-characteristics-prices-and-providers_75e50b2a/9302bf46-en.pdf)\n  * [No. 39, June 20, 2025, The effects of generative AI on productivity, innovation and entrepreneurship](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/the-effects-of-generative-ai-on-productivity-innovation-and-entrepreneurship_da1d085d/b21df222-en.pdf)\n  * [No. 40, June 27, 2025, Is generative AI a General Purpose Technology? Implications for productivity and policy](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/is-generative-ai-a-general-purpose-technology_6c76e7b2/704e2d12-en.pdf)\n  * [No. 41, June 30, 2025, Macroeconomic productivity gains from Artificial Intelligence in G7 economies](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/macroeconomic-productivity-gains-from-artificial-intelligence-in-g7-economies_dcf91c3e/a5319ab5-en.pdf)\n  * [No. 42, June 30, 2025, AI and the future of social protection in OECD countries](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/ai-and-the-future-of-social-protection-in-oecd-countries_038f49ed/7b245f7e-en.pdf)\n  * [No. 43, July 31, 2025, Exploring win-win outcomes of algorithmic management: Lessons from a laboratory experiment on worker consultation](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/07/exploring-win-win-outcomes-of-algorithmic-management_88216705/84b59397-en.pdf)\n  * [No. 44, August 14, 2025, AI openness: A primer for policymakers](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/08/ai-openness_958d292b/02f73362-en.pdf)\n  * [No. 45, September 25, 2025, Leveraging artificial intelligence to support students with special education needs](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/09/leveraging-artificial-intelligence-to-support-students-with-special-education-needs_ebc80fc8/1e3dffa9-en.pdf)\n  * [No. 46, September 25, 2025, How are AI developers managing risks? Insights from responses to the reporting framework of the Hiroshima AI Process Code of Conduct](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/09/how-are-ai-developers-managing-risks_fbaeb3ad/658c2ad6-en.pdf)\n  * [No. 47, September 26, 2025, Advancing the measurement of investments in artificial intelligence](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/09/advancing-the-measurement-of-investments-in-artificial-intelligence_7f58ff65/13e0da2f-en.pdf)\n  * [No. 48, October 3, 2025, Mapping relevant data collection mechanisms for AI training](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/10/mapping-relevant-data-collection-mechanisms-for-ai-training_62921889/3264cd4c-en.pdf)\n  * [No. 49, October 29, 2025, Measuring domestic public cloud compute availability for artificial intelligence](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/10/measuring-domestic-public-cloud-compute-availability-for-artificial-intelligence_39fa6b0e/8602a322-en.pdf)\n  * [No. 50, December 1, 2025, Artificial intelligence in Asia's financial sector: A review of country policies](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/artificial-intelligence-in-asia-s-financial-sector_b8532d0b/3385bbd8-en.pdf)\n  * [No. 51, December 8, 2025, AI and the global productivity divide: Fuel for the fast or a lift for the laggards?](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/ai-and-the-global-productivity-divide_f47026c5/c315ea90-en.pdf)\n  * [No. 52, December 11, 2025, AI adoption in the education system: International insights and policy considerations for Italy](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/ai-adoption-in-the-education-system_43251cf0/69bd0a4a-en.pdf)\n  * [No. 53, January 27, 2026, Supervision of artificial intelligence in finance: Challenges, policies and practices](https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/01/supervision-of-artificial-intelligence-in-finance_1295e5e2/92743dc1-en.pdf)\n  * [No. 54, February 3, 2026, Exploring possible AI trajectories through 2030](https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/02/exploring-possible-ai-trajectories-through-2030_b6fb75d9/cb41117a-en.pdf)\n  * [No. 55, February 10, 2026, Trends in AI incidents and hazards reported by the media](https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/02/trends-in-ai-incidents-and-hazards-reported-by-the-media_7c824ca9/4f5ff43c-en.pdf)\n  * [No. 56, February 13, 2026, The agentic AI landscape and its conceptual foundations](https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/02/the-agentic-ai-landscape-and-its-conceptual-foundations_a9d4b451/396cf758-en.pdf)\n* [OECD-Bericht zu Künstlicher Intelligenz in Deutschland](https://www.ki-strategie-deutschland.de/files/downloads/OECD-Bericht_K%C3%BCnstlicher_Intelligenz_in_Deutschland.pdf)\n* [OECD Digital Economy Papers, No. 341, November 2022, Measuring the Environmental Impacts of Artificial Intelligence Computer and Applications: The AI Footprint](https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/11/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_3dddded5/7babf571-en.pdf)\n* [OECD Legal Instruments, Recommendation of the Council on Artificial Intelligence, adopted May 22, 2019, amended May 3, 2024](https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449)\n* [Open, Useful and Re-usable data Index: 2019](https://www.oecd.org/content/dam/oecd/en/publications/reports/2020/03/open-useful-and-re-usable-data-ourdata-index-2019_4c070c33/45f6de2d-en.pdf) |  (OURdata)\n* [Measuring the environmental impacts of artificial intelligence compute and applications](https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html)\n* [The Bias Assessment Metrics and Measures Repository](https://oecd.ai/en/catalogue/tools/the-bias-assessment-metrics-and-measures-repository)\n\n#### OSCE\n\n* [#SAIFE Resource Hub: Spotlight on Artificial Intelligence and Freedom of Expression](https://www.osce.org/saife/index.html)\n* [Artificial Intelligence and Disinformation: State-Aligned Information Operations and the Distortion of the Public Sphere](https://www.osce.org/files/f/documents/e/b/522166.pdf) | July 2022\n* [Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual](https://www.osce.org/files/f/documents/8/f/510332_1.pdf)\n\n#### NATO\n\n* [AI in Precision Persuasion. Unveiling Tactics and Risks on Social Media](https://stratcomcoe.org/publications/ai-in-precision-persuasion-unveiling-tactics-and-risks-on-social-media/309)\n* [Narrative Detection and Topic Modelling in the Baltics](https://stratcomcoe.org/publications/narrative-detection-and-topic-modelling-in-the-baltics/303)\n* [\"NATO-Mation\": Strategies for Leading in the Age of Artificial Intelligence](https://www.ulib.sk/files/english/nato-library/collections/monographs/ndc-research-paper/ndc_rp_15.pdf), NDC Research Paper No. 15, December 2020\n* [Summary of the NATO Artificial Intelligence Strategy](https://www.nato.int/cps/en/natohq/official_texts_187617.htm) | October 22, 2021\n  * [An Artificial Intelligence Strategy for NATO](https://www.nato.int/docu/review/articles/2021/10/25/an-artificial-intelligence-strategy-for-nato/index.html) | October 25, 2021\n* [Summary of NATO's revised Artificial Intelligence strategy](https://www.nato.int/cps/en/natohq/official_texts_227237.htm) | July 10, 2024\n* [Virtual Manipulation Brief 2025: From War and Fear to Confusion and Uncertainty](https://stratcomcoe.org/publications/download/VMB-Final-5aa5d.pdf) | NATO Strategic Communications Centre of Excellence, June 2, 2025\n\n#### Indigenous and Tribal Governments and Nations\n\n* [Report of the Artificial Intelligence, Data Sovereignty, and Cybersecurity Task Force](https://www.cherokee.org/media/0ipldvul/task-force-report-on-ai-data-sovereignty-cybersecurity.pdf) | Cherokee Nation, 2025\n\n#### United Nations\n\n* [A Framework for Ethical AI at the United Nations, March 15, 2021](https://unite.un.org/sites/unite.un.org/files/unite_paper_-_ethical_ai_at_the_un.pdf) | Office for Information and Communications Technology\n* [A matter of choice: People and possibilities in the age of AI](https://hdr.undp.org/system/files/documents/global-report-document/hdr2025reporten.pdf) | UNDP Human Development Report 2025\n* [Casinos, cyber fraud, and trafficking in persons for forced criminality in Southeast Asia](https://www.unodc.org/roseap/uploads/documents/Publications/2023/TiP_for_FC_Policy_Report.pdf) | United Nations Office on Drugs and Crime (UNODC), September 2023\n* [Governing AI for Humanity, Final Report](https://digitallibrary.un.org/record/4062495/files/1416782-EN.pdf?ln=en) | September 2024\n* [High-Level Advisory Body on Artificial Intelligence](https://www.un.org/techenvoy/ai-advisory-body) | Office of the Secretary-General's Envoy on Technology\n* [Office of the United Nations High Commissioner for Human Rights](https://www.ohchr.org/sites/default/files/documents/issues/business/b-tech/taxonomy-GenAI-Human-Rights-Harms.pdf)\n* UNESCO\n  * [AI and education: guidance for policy-makers](https://unesdoc.unesco.org/ark:/48223/pf0000376709)\n  * [AI and the future of education: disruptions, dilemmas and directions](https://unesdoc.unesco.org/ark:/48223/pf0000395236/PDF/395236eng.pdf.multi) | September 2025\n  * [Artificial Intelligence: examples of ethical dilemmas](https://www.unesco.org/en/artificial-intelligence/recommendation-ethics/cases)\n  * [Caribbean Artificial Intelligence Policy Roadmap](https://unesdoc.unesco.org/ark:/48223/pf0000391996/PDF/391996eng.pdf.multi)\n  * [Consultation paper on AI regulation: emerging approaches across the world](https://unesdoc.unesco.org/ark:/48223/pf0000390979)\n  * [Global AI Ethics and Governance Observatory](https://www.unesco.org/ethics-ai/en)\n  * [Pathways on capacity building for AI supervisory authorities: insights and recommendations from the 1st UNESCO expert roundtable on AI supervision](https://unesdoc.unesco.org/ark:/48223/pf0000396637) | UNESCO and Dutch Authority for Digital Infrastructure, 2025\n  * [Readiness assessment methodology: a tool of the Recommendation on the Ethics of Artificial Intelligence](https://www.unesco.org/en/articles/readiness-assessment-methodology-tool-recommendation-ethics-artificial-intelligence)\n  * [Recommendation on the Ethics of Artificial Intelligence](https://unesdoc.unesco.org/ark:/48223/pf0000381137/PDF/381137eng.pdf.multi) | Adopted on 23 November 2021\n  * [Smarter, smaller, stronger: resource-efficient generative Al & the future of digital transformation](https://unesdoc.unesco.org/ark:/48223/pf0000394521.locale=en) | 2025\n* [Policy guidance on AI for children, Recommendations for building AI policies and systems that uphold child rights](https://www.unicef.org/innocenti/media/1341/file/UNICEF-Global-Insight-policy-guidance-AI-children-2.0-2021.pdf) | UNICEF\n* [Principles for the ethical use of artificial intelligence in the United Nations system](https://unsceb.org/sites/default/files/2023-03/CEB_2022_2_Add.1%20%28AI%20ethics%20principles%29.pdf) | Chief Executives Board for Coordination, 2022-10-27\n* [Terms of Reference and Modalities for the Establishment and Functioning of the Independent International Scientific Panel on Artificial Intelligence and the Global Dialogue on Artificial Intelligence Governance](https://documents.un.org/doc/undoc/ltd/n25/222/68/pdf/n2522268.pdf)\n\n### Documents in Legal Genres\n\nLegislation, litigation, and other legal materials relevant to AI policy and governance.\n\n* [AI Learning Agenda](https://www.ncleg.gov/Sessions/2025/Bills/Senate/PDF/S747v0.pdf) | General Assembly of North Carolina, Session 2025, Senate Bill DRS245362-LR-142A\t\n* [An Act Addressing Innovations in Artificial Intelligence](https://www.cga.ct.gov/2025/ba/pdf/2025SB-01249-R000606-BA.pdf) | OLR Bill Analysis SB 1249\n* [An Act relating to artificial intelligence; requiring disclosure of deepfakes in campaign communications; relating to cybersecurity; and relating to data privacy.](https://www.akleg.gov/basis/Bill/Detail/33?Root=HB306) | Alaska State Legislature, HB 306\n* [Agenda Book for Advisory Committee on Evidence Rules – Panel on Artificial Intelligence and the Rules of Evidence](https://www.uscourts.gov/sites/default/files/2024-04_agenda_book_for_evidence_rules_meeting_final.pdf) | April 19, 2024  \n* [Algorithmic Accountability Act of 2023](https://www.govinfo.gov/app/details/BILLS-118hr5628ih/)\n* [Arizona, House Bill 2685](https://www.azleg.gov/legtext/55leg/2r/bills/hb2685h.htm)\n* [Australia, Privacy Act 1988](https://www.legislation.gov.au/Details/C2014C00076)\n* [California, Civil Rights Council - First Modifications to Proposed Employment Regulations on Automated-Decision Systems, Title 2, California Code of Regulations](https://calcivilrights.ca.gov/wp-content/uploads/sites/32/2024/10/First-Modifications-to-Text-of-Proposed-Modifications-to-Employment-Regulations-Regarding-Automated-Decision-Systems.pdf)* \n* [California, Consumer Privacy Act of 2018](https://leginfo.legislature.ca.gov/faces/codes_displayText.xhtml?division=3.&part=4.&lawCode=CIV&title=1.81.5) | Civil Code - DIVISION 3. OBLIGATIONS [1427 - 3273.69]\n* [California, Senate Bill No. 53](https://legiscan.com/CA/text/SB53/id/3271094)\n* [Cherkin et al. v. PowerSchool Holdings Inc. N.D. Cal. May 2024 – EdTech Privacy Class Action](https://s3.documentcloud.org/documents/25260275/cherkin-v-powerschool-complaint-20240506.pdf)\n* [Colorado, SB24-205 Consumer Protections for Artificial Intelligence, Concerning consumer protections in interactions with artificial intelligence systems](https://leg.colorado.gov/bills/SB24-205)\n* [Decoupling America’s Artificial Intelligence Capabilities from China Act](https://www.hawley.senate.gov/wp-content/uploads/2025/01/Hawley-Decoupling-Americas-Artificial-Intelligence-Capabilities-from-China-Act.pdf) | U.S. Senate, 119th Congress, introduced by Senator Josh Hawley, January 2025\n* [European Union, General Data Protection Regulation](https://gdpr-info.eu/) |  (GDPR)\n  * [Article 22 EU GDPR \"Automated individual decision-making, including profiling\"](https://www.privacy-regulation.eu/en/article-22-automated-individual-decision-making-including-profiling-GDPR.htm)\n* [Popa v. Harriet Carter Gifts Inc. W.D. Pa. Mar. 2025 – Class Action on Digital Wiretapping](https://app.midpage.ai/document/popa-v-harriet-carter-gifts-10829535?refG=true)\n* [Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government](https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government) | Executive Order 13960 (2020-12-03)\n* [Facial Recognition and Biometric Technology Moratorium Act of 2020](https://drive.google.com/file/d/1gkTcjFtieMQdsQ01dmDa49B6HY9ZyKr8/view)\n* [Federal Consumer Online Privacy Rights Act](https://www.consumerprivacyact.com/federal/) | (COPRA)\n* [GDPR Complaint Filed by noyb Against OpenAI](https://noyb.eu/sites/default/files/2024-04/OpenAI%20Complaint_EN_redacted.pdf) | Austria DSB, April 2024\n* [Germany, Bundesrat Drucksache 222/24 - Entwurf eines Gesetzes zum strafrechtlichen Schutz von Persönlichkeitsrechten vor Deepfakes](https://tinyurl.com/d7r8baz8) |  (Draft Law on the Criminal Protection of Personality Rights from Deepfakes)\n* [Illinois, Biometric Information Privacy Act](https://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=3004&ChapterID=57)\n* [In re Clearview AI Inc. N.D. Ill. Aug. 2022 – MDL Opinion on Amended Complaint & Retail Defendants](https://cases.justia.com/federal/district-courts/illinois/ilndce/1:2021cv00135/395030/407/0.pdf?ts=1660231616)\n* [Justice in Policing Act](https://democrats-judiciary.house.gov/issues/issue/?IssueID=14924)\n* [National Conference of State Legislatures 2020 Consumer Data Privacy Legislation](https://www.ncsl.org/technology-and-communication/2020-consumer-data-privacy-legislation) | (NCSL)\n* [Nebraska, LB1203 - Regulate artificial intelligence in media and political advertisements under the Nebraska Political Accountability and Disclosure Act](https://nebraskalegislature.gov/bills/view_bill.php?DocumentID=55088)\n* [The New York Times Company v. Microsoft Corp. OpenAI Inc. et al. December 2023 – Complaint](https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec2023.pdf)\n* [The New York Times Company v. Microsoft Corporation OpenAI Inc. et al. November 2024 – Opinion & Order on Discovery Dispute](https://www.sdnyblog.com/files/2024/11/23-cv-11195-SHS-OTW-NYT-v.-Microsoft-Opinion.pdf)\n* [Rhode Island, Executive Order 24-06: Artificial Intelligence and Data Centers of Excellence](https://governor.ri.gov/executive-orders/executive-order-24-06)\n* [Silverman et al. v. Meta Platforms Inc. N.D. Cal. 2023 Class Action Complaint](https://storage.courtlistener.com/recap/gov.uscourts.cand.415175/gov.uscourts.cand.415175.1.0_3.pdf)\n* [State of North Carolina Executive Order No. 24, Advancing Trustworthy Artificial Intelligence That Benefits All North Carolinians](https://governor.nc.gov/executive-order-no-24-advancing-trustworthy-artificial-intelligence-benefits-all-north-carolinians/open) | September 2, 2025\n* [Texas draft of responsible AI bill by Capriglione](https://www.mba.org/docs/default-source/policy/state-relations/draft_texas-ai_10.28.24.pdf?sfvrsn=9f83267e_1) | October 28, 2024\n* [Thaler v. Perlmutter March 2025 – Appellate Opinion on Copyright and Artificial Intelligence](https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-5233.pdf)\n* [Virginia, Consumer Data Protection Act](https://law.lis.virginia.gov/vacodefull/title59.1/chapter53/)\n* [Washington State, SB 6513 - 2019-20](https://apps.leg.wa.gov/billsummary/?BillNumber=6513&Year=2020&Initiative=false)\n* [United States Congress, 118th Congress, H.R.5586 - DEEPFAKES Accountability Act](https://www.congress.gov/bill/118th-congress/house-bill/5586/text) | 2023-2024\n* [United States Congress, 118th Congress, H.R. 9720, AI Incident Reporting and Security Enhancement Act](https://science.house.gov/bills?ID=95D5A008-EA1A-4D43-A363-DC2D129DFDCD) | 2023-2024\n* [United States Congress, 118th Congress, S.4769 - VET Artificial Intelligence Act](https://www.congress.gov/bill/118th-congress/senate-bill/4769/text) | 2023-2024\n* [Willis v. Bank National Association as Trustee Igloo Series Trust LLC](https://caselaw.findlaw.com/court/us-dis-crt-n-d-tex-dal-div/117272437.html) | 2025\n\n## Education Resources\n\n### Comprehensive Software Examples and Tutorials\n\nThis section is a curated collection of guides and tutorials that simplify responsible ML implementation. It spans from basic model interpretability to advanced fairness techniques. Suitable for both novices and experts, the resources cover topics like COMPAS fairness analyses and explainable machine learning via counterfactuals.\n\n* [COMPAS Analysis Using Aequitas](https://github.com/dssg/aequitas/blob/master/docs/source/examples/compas_demo.ipynb) | ![](https://img.shields.io/github/stars/dssg/aequitas?style=social)\n* [Explaining Quantitative Measures of Fairness with SHAP](https://github.com/slundberg/shap/blob/master/notebooks/overviews/Explaining%20quantitative%20measures%20of%20fairness.ipynb) | ![](https://img.shields.io/github/stars/slundberg/shap?style=social)\n* [Getting a Window into your Black Box Model](http://projects.rajivshah.com/inter/ReasonCode_NFL.html)\n* H20.ai\n* [From GLM to GBM Part 1](https://www.h2o.ai/blog/from-glm-to-gbm-part-1/)\n* [From GLM to GBM Part 2](https://www.h2o.ai/blog/from-glm-to-gbm-part-2/)\n* [IML](https://mybinder.org/v2/gh/christophM/iml/master?filepath=./notebooks/tutorial-intro.ipynb)\n* [Interpretable Machine Learning with Python](https://github.com/jphall663/interpretable_machine_learning_with_python) | ![](https://img.shields.io/github/stars/jphall663/interpretable_machine_learning_with_python?style=social)\n* [Interpreting Machine Learning Models with the iml Package](http://uc-r.github.io/iml-pkg)\n* [Interpretable Machine Learning using Counterfactuals](https://docs.seldon.io/projects/alibi/en/v0.2.0/examples/cf_mnist.html)\n* [Machine Learning Explainability by Kaggle Learn](https://www.kaggle.com/learn/machine-learning-explainability)\n* [Model Interpretability with DALEX](http://uc-r.github.io/dalex)\n  * **Model Interpretation series by Dipanjan (DJ) Sarkar**\n    * [Hands-on Machine Learning Model Interpretation](https://towardsdatascience.com/explainable-artificial-intelligence-part-3-hands-on-machine-learning-model-interpretation-e8ebe5afc608)\n    * [Interpreting Deep Learning Models for Computer Vision](https://medium.com/google-developer-experts/interpreting-deep-learning-models-for-computer-vision-f95683e23c1d)\n    * [Model Interpretation Strategies](https://towardsdatascience.com/explainable-artificial-intelligence-part-2-model-interpretation-strategies-75d4afa6b739)\n    * [The Importance of Human Interpretable Machine Learning](https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476)\n* [Partial Dependence Plots in R](https://journal.r-project.org/archive/2017/RJ-2017-016/)\n* PiML\n  * [PiML Medium Tutorials](https://piml.medium.com)\n  * [PiML-Toolbox Examples](https://github.com/SelfExplainML/PiML-Toolbox/tree/main/examples) | ![](https://img.shields.io/github/stars/SelfExplainML/PiML-Toolbox?style=social)\n* [Reliable-and-Trustworthy-AI-Notebooks](https://github.com/ClementSicard/Reliable-and-Trustworthy-AI-Notebooks) | ![](https://img.shields.io/github/stars/ClementSicard/Reliable-and-Trustworthy-AI-Notebooks?style=social)\n* [Saliency Maps for Deep Learning](https://medium.com/@thelastalias/saliency-maps-for-deep-learning-part-1-vanilla-gradient-1d0665de3284)\n* [Visualizing ML Models with LIME](http://uc-r.github.io/lime)\n* [Visualizing and debugging deep convolutional networks](https://rohitghosh.github.io/2018/01/05/visualising-debugging-deep-neural-networks/)\n* [What does a CNN see?](https://colab.research.google.com/drive/1xM6UZ9OdpGDnHBljZ0RglHV_kBrZ4e-9)\n\n### Free-ish Books\n\nThis section contains books that can be reasonably described as free, including some \"historical\" books dealing broadly with ethical and responsible tech.\n\n* [Adversarial Model Analysis](https://ama.drwhy.ai/) | Przemyslaw Biecek, 2023\n* [An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI](https://h2o.ai/content/dam/h2o/en/marketing/documents/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf) | Patrick Hall and Navdeep Gill, 2019, Second Edition\n* [Artificial Intelligence and Fundamental Rights: The AI Act of the European Union and its implications for global technology regulation](https://irdt-schriften.uni-trier.de/index.php/irdt/catalog/view/6/6/50) | Trier Studies on Digital Law, Volume 4\n* [Case Studies in Information and Computer Ethics](https://archive.org/details/unset0000unse_l0l0) | Richard A. Spinello, 1997\n* [Case Studies in Information Technology Ethics](https://archive.org/details/casestudiesininf02edspin) | Richard A. Spinello, 2003, Second Edition\n* [Computer and Information Ethics](https://archive.org/details/computerinformat0000wood_q3r6) | Marsha Cook Woodbury, 2003\n* [Computer Ethics: Analyzing Information Technology](https://archive.org/details/computerethicsan0004edjohn) | Deborah G. Johnson and Keith W. Miller, 2009,  Fourth Edition\n* [Computer Power and Human Reason: From Judgment to Calculation](https://archive.org/details/computerpowerhum0000weiz_v0i3/mode/2up) | Joseph Weizenbaum, 1976\n* [Computers, Ethics, and Society](https://archive.org/details/computersethicss0000unse) | M. David Ermann, Mary B. Williams, and Claudio Gutierrez, 1990\n* [Controlling Technology: Ethics and the Responsible Engineer](https://archive.org/details/controllingtechn0000unge_y4t3) | Stephen H. Unger, 1982, First Edition\n* [Controlling Technology: Ethics and the Responsible Engineer](https://archive.org/details/controllingtechn0000unge) | Stephen H. Unger, 1994, Second Edition\n* [Ethical Aspects of Information Technology](https://archive.org/details/ethicalaspectsof00spin) | Richard A. Spinello, 1995\n* [Ethics for people who work in tech](https://ethicsforpeoplewhoworkintech.com/)\n* [Ethics in Information Technology](https://archive.org/details/ethicsininformat0000reyn) | George Reynolds, 2002, Instructor's Edition\n* [Ethics in Information Technology](https://archive.org/details/ethicsininformat00reyn) | George Reynolds, 2002\n* [Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python](https://ema.drwhy.ai/) | Przemyslaw Biecek and Tomasz Burzykowski, 2020\n* [Fairness and Machine Learning: Limitations and Opportunities](https://fairmlbook.org/) | Solon Barocas, Moritz Hardt, and Arvind Narayanan, 2022\n* [Fueling Our Future: A Dialogue about Technology, Ethics, Public Policy, and Remedial Action](https://archive.org/details/fuelingourfuture0000unse/mode/2up) | Ed Dreby and Keith Helmuth, contributors, and Judy Lumb, editor, 2009\n* [How Humans Judge Machines](https://archive.org/details/mit_press_book_9780262363266) | César A. Hidalgo, Diana Orghian, Jordi Albo-Canals, Filipa de Almeida, and Natalia Martin, 2021\n* [Information Technology Ethics: Cultural Perspectives](https://archive.org/details/informationtechn0000unse_k8c9) | Soraj Hongladarom and Charles Ess, 2007\n* [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/) | Christoph Molnar, 2021\n   * [christophM/interpretable-ml-book](https://github.com/christophM/interpretable-ml-book) | ![](https://img.shields.io/github/stars/christophM/interpretable-ml-book?style=social)\n* [Normal Accidents: Living with High-Risk Technologies with a New Afterword and a Postscript on the Y2K Problem](https://archive.org/details/normalaccidentsl00perr) | Charles Perrow, 1999\n* [Normal Accidents: Living with High-Risk Technologies](https://archive.org/details/normalaccidentsl0000perr) | Charles Perrow, 1984\n* [Regulating under Uncertainty: Governance Options for Generative AI](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4918704) | Florence G'sell\n* [Responsible Machine Learning: Actionable Strategies for Mitigating Risks & Driving Adoption](https://info.h2o.ai/rs/644-PKX-778/images/OReilly_Responsible_ML_eBook.pdf) | Patrick Hall, Navdeep Gill, and Benjamin Cox, 2021\n* [Science and Technology Ethics](https://archive.org/details/sciencetechnolog0000unse_k7m6) | Raymond E. Spier (editor), 200\n* [Society, Ethics, and Technology](https://archive.org/details/societyethicstec0000unse) | Morton E. Winston and Ralph D. Edelbach, 2003, Second Edition\n* [Society, Ethics, and Technology](https://archive.org/details/societyethicstec00edel) | Morton E. Winston and Ralph D. Edelbach, 2006, Third Edition\n* [Society, Ethics, and Technology](https://archive.org/details/societyethicstec00wins) | Morton E. Winston and Ralph D. Edelbach, 2000, First Edition\n* [The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/0AD007641DE27F837A3A16DBC0888DD1/9781009367813AR.pdf/The_Cambridge_Handbook_of_the_Law__Ethics_and_Policy_of_Artificial_Intelligence.pdf?event-type=FTLA) | Nathalie A. Smuha, ed., 2025\n* [Towards a Code of Ethics for Artificial Intelligence](https://archive.org/details/towardscodeofeth0000bodd) | Paula Boddington, 2017\n* [Trustworthy AI: African Perspectives](https://link.springer.com/book/10.1007/978-3-031-75674-0) | Damian Okaibedi Eke, Kutoma Wakunuma, Simisola Akintoye, and George Ogoh, eds., 2025\n* [Trustworthy Machine Learning: Concepts for Developing Accurate, Fair, Robust, Explainable, Transparent, Inclusive, Empowering, and Beneficial Machine Learning Systems](http://www.trustworthymachinelearning.com/) | Kush R. Varshney, 2022\n* [Who Shall Live? Medicine, Technology, Ethics](https://archive.org/details/whoshalllivemedi0000hous) | Kenneth Vaux (editor), 1970\n\n### Glossaries and Dictionaries\n\nThis section features a collection of glossaries and dictionaries that are geared toward defining terms in ML, including some \"historical\" dictionaries.\n\n* [50 AI terms every beginner should know](https://www.telusinternational.com/insights/ai-data/article/50-beginner-ai-terms-you-should-know) | TELUS International\n* [A Glossary of AI Jargon: 29 AI Terms You Should Know](https://www.makeuseof.com/glossary-ai-jargon-terms/) | MakeUseOf\n* [A Multilingual Dictionary of Artificial Intelligence](https://archive.org/details/multilingualdict0000voll) | Otto Vollnhals, 1992 (English, German, French, Spanish, Italian)\n* [A.I. For Anyone: The A-Z of AI](https://www.aiforanyone.org/glossary)\n* [Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations](https://csrc.nist.gov/pubs/ai/100/2/e2023/final) | National Institute of Standards and Technology (NIST), NIST AI 100-2 E2023\n* [AI dictionary: Be a native speaker of Artificial Intelligence](https://dataconomy.com/2022/04/23/artificial-intelligence-terms-ai-glossary/) | Dataconomy\n* [AI From A to Z: The Generative AI Glossary for Business Leaders](https://www.salesforce.com/blog/generative-ai-glossary/) | Salesforce\n* [AI Terms Glossary](https://www.moveworks.com/us/en/resources/ai-terms-glossary) | Moveworks\n* [Appen Artificial Intelligence Glossary](https://appen.com/ai-glossary/)\n* [Artificial intelligence  glossary](https://post.parliament.uk/artificial-intelligence-ai-glossary/) | UK Parliament\n* [Artificial Intelligence  Terms: A to Z Glossary](https://www.coursera.org/articles/ai-terms) | Coursera\n* [Artificial intelligence and illusions of understanding in scientific research](https://www.nature.com/articles/s41586-024-07146-0.epdf?sharing_token=cbht6Q72InY18AtY6FiVM9RgN0jAjWel9jnR3ZoTv0Ni_LuMWrIZy_SmHlNQlu9tG1u0SCK_wTYxy6bvMe6U_BE3vc5yFmZEpTbIVJozkVYsOei9LdPpNr_wZzvTp4stmzGM54z-riqwhUCk0DD6_YkY_jcgZBnXR8P_8vyFvYpiCtjFrvczN9Lm6NhmrePm) | (glossary on second page)\n* [Artificial Intelligence Definitions](https://hai.stanford.edu/sites/default/files/2023-03/AI-Key-Terms-Glossary-Definition.pdf) | Stanford University HAI\n* [Artificial Intelligence Glossary](https://www.siemens.com/global/en/company/stories/artificial-intelligence/ai-glossary.html) | Siemens\n* [Artificial Intelligence Terminology: A Glossary for Beginners](https://connect.comptia.org/content/articles/artificial-intelligence-terminology) | CompTIA\n* [Brookings: The Brookings glossary of AI and emerging technologies](https://www.brookings.edu/articles/the-brookings-glossary-of-ai-and-emerging-technologies/)\n* [Built In, Responsible AI Explained](https://builtin.com/artificial-intelligence/responsible-ai)\n* [Center for Security and Emerging Technology: Glossary](https://cset.georgetown.edu/glossary/)\n* [Collins Dictionary of Artificial Intelligence](https://archive.org/details/collinsdictionar0000unse_w3w7) | Raoul Smith, 1990\n* [Council of Europe Artificial Intelligence Glossary](https://www.coe.int/en/web/artificial-intelligence/glossary)\n* [Dictionary of Artificial Intelligence & Robotics](https://archive.org/details/dictionaryofarti00rose) | Jerry M. Rosenberg, 1986\n* [Dictionary of Artificial Intelligence](https://archive.org/details/dictionaryofarti0000merc) | Dennis Mercadal, 1990\n* [Dictionary of Cognitive Science: Neuroscience, Psychology, Artificial Intelligence, Linguistics, and Philosophy](https://archive.org/details/dictionaryofcogn0000unse) | Oliver Houdé, 2004\n* [EU-U.S. Terminology and Taxonomy for Artificial Intelligence](https://digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy-artificial-intelligence-second-edition) | European Commission, Second Edition\n* [G2: 70+ A to Z Artificial Intelligence Terms in Technology](https://www.g2.com/articles/artificial-intelligence-terms)\n* [General Services Administration: AI Guide for Government: Key AI terminology](https://coe.gsa.gov/coe/ai-guide-for-government/what-is-ai-key-terminology/)\n* [Glossary for Discussion of Ethics of Autonomous and Intelligent Systems](https://standards.ieee.org/wp-content/uploads/import/documents/other/eadv2_glossary.pdf) | IEEE, Version 1\n* [Glossary of artificial intelligence](https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence) | Wikipedia\n* [Glossary of human-centric artificial intelligence](https://publications.jrc.ec.europa.eu/repository/handle/JRC129614) | European Commission\n* [Google Developers Machine Learning Glossary](https://developers.google.com/machine-learning/glossary)\n* [H2O.ai Glossary](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/glossary.html)\n* IAPP\n  * [Glossary of Privacy Terms](https://iapp.org/resources/glossary/)\n  * [International Definitions of Artificial Intelligence](https://iapp.org/media/pdf/resource_center/international_definitions_of_ai.pdf)\n  * [Key Terms for AI Governance](https://iapp.org/resources/article/key-terms-for-ai-governance/)\n* [IBM AI glossary](https://www.ibm.com/cloud/architecture/architecture/practices/cognitive-glossary/)\n* [International Dictionary of Artificial Intelligence](https://archive.org/details/internationaldic0000rayn_t1n5/mode/2up) | William J. Raynor, Jr, 2009, Second Edition\n* [ISO/IEC DIS 22989 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology](https://www.iso.org/obp/ui/fr/#iso:std:iso-iec:22989:dis:ed-1:v1:en)\n* [Lexicon](https://www.ai.mil/Lexicon/) | Chief Digital and Artificial Intelligence Office (CDAO)\n* [Open Access Vocabulary](https://repository.ifla.org/bitstream/123456789/3272/1/Open%20Access%20Vocabulary%20Feb2024%20v2.pdf)\n* [TechTarget: Artificial intelligence glossary: 60+ terms to know](https://www.techtarget.com/whatis/feature/Artificial-intelligence-glossary-60-terms-to-know)\n* [Terms from Artificial Intelligence: humans at the heart of algorithms](https://alandix.com/glossary/aibook)\n* [The Alan Turing Institute: Data science and AI glossary](https://www.turing.ac.uk/news/data-science-and-ai-glossary)\n* [The Facts on File Dictionary of Artificial Intelligence](https://archive.org/details/factsonfiledicti00smit) | Raoul Smith, 1989\n* [The International Dictionary of Artificial Intelligence](https://archive.org/details/internationaldic0000rayn/mode/2up) | William J. Raynor, Jr, 1999, First Edition\n* [The Language of Trustworthy AI: An In-Depth Glossary of Terms](https://airc.nist.gov/AI_RMF_Knowledge_Base/Glossary) | National Institute of Standards and Technology (NIST)\n* [The Machine Learning Dictionary](https://www.cse.unsw.edu.au/~billw/mldict.html) | University of New South Wales, Bill Wilson,\n* [Towards AI, Generative AI Terminology — An Evolving Taxonomy To Get You Started](https://towardsai.net/p/machine-learning/generative-ai-terminology-an-evolving-taxonomy-to-get-you-started)\n* [Vocabulary of AI Risks](https://delaramglp.github.io/vair/) | VAIR\n\n### Open-ish Classes\n\nThis section features a selection of educational courses focused on ethical considerations and best practices in ML. The classes range from introductory courses on data ethics to specialized training in fairness and trustworthy deep learning.\n\n* [An Introduction to Data Ethics](https://www.scu.edu/ethics/focus-areas/technology-ethics/resources/an-introduction-to-data-ethics/)\n* [Awesome LLM Courses](https://github.com/wikit-ai/awesome-llm-courses) | ![](https://img.shields.io/github/stars/wikit-ai/awesome-llm-courses?style=social)\n* [AWS Skill Builder](https://skillbuilder.aws/)\n* [Build a Large Language Model - From Scratch](https://github.com/rasbt/LLMs-from-scratch/tree/main) | ![](https://img.shields.io/github/stars/rasbt/LLMs-from-scratch?style=social)\n* [Certified Ethical Emerging Technologist](https://certnexus.com/certification/ceet/)\n* [Computational Ethics for NLP](http://demo.clab.cs.cmu.edu/ethical_nlp/) | Carnegie Mellon University\n* [CS 4910 - Special Topics in Computer Science: Algorithm Audits](https://sapiezynski.com/cs4910.html) | Piotr Sapieżyński\n* [CS103F: Ethical Foundations of Computer Science](https://www.cs.utexas.edu/~ans/classes/cs109/schedule.html)\n* [Data Ethics course](http://ethics.fast.ai/syllabus) | Fast.ai\n* [DeepLearning.AI](https://www.deeplearning.ai/courses/)\n* [Disability-Centered AI And Ethics MOOC](https://oecd.ai/en/catalogue/tools/disability-centered-ai-and-ethics-mooc) | OECD.AI\n* [ETH Zürich ReliableAI 2022 Course Project repository](https://github.com/angelognazzo/Reliable-Trustworthy-AI) | ![](https://img.shields.io/github/stars/angelognazzo/Reliable-Trustworthy-AI?style=social)\n* [Fairness in Machine Learning](https://fairmlclass.github.io/)\n* [Generative AI for Educators](https://grow.google/ai-for-educators/) | Grow with Google\n* [Generative AI for Everyone](https://www.coursera.org/learn/generative-ai-for-everyone) | Coursera, DeepLearning.AI\n* [Generative AI with Large Language Models](https://www.coursera.org/learn/generative-ai-with-llms) | Coursera, DeepLearning.AI\n* [Google Cloud Skills Boost](https://www.cloudskillsboost.google/)\n  * [Attention Mechanism](https://www.cloudskillsboost.google/course_templates/537)\n  * [Create Image Captioning Models](https://www.cloudskillsboost.google/course_templates/542)\n  * [Encoder-Decoder Architecture](https://www.cloudskillsboost.google/course_templates/543)\n  * [Introduction to Generative AI](https://www.cloudskillsboost.google/course_templates/536)\n  * [Introduction to Image Generation](https://www.cloudskillsboost.google/course_templates/541)\n  * [Introduction to Large Language Models](https://www.cloudskillsboost.google/course_templates/539)\n  * [Introduction to Responsible AI](https://www.cloudskillsboost.google/course_templates/554)\n  * [Introduction to Vertex AI Studio](https://www.cloudskillsboost.google/course_templates/552)\n  * [Transformer Models and BERT Model](https://www.cloudskillsboost.google/course_templates/538)\n* [Human-Centered Machine Learning](http://courses.mpi-sws.org/hcml-ws18/)\n* [IBM SkillsBuild](https://sb-auth.skillsbuild.org/)\n* [INFO 4270: Ethics and Policy in Data Science](https://docs.google.com/document/d/1GV97qqvjQNvyM2I01vuRaAwHe9pQAZ9pbP7KkKveg1o/)\n* [Introduction to AI Ethics](https://www.kaggle.com/code/var0101/introduction-to-ai-ethics)\n* [Introduction to Generative AI](https://www.coursera.org/learn/introduction-to-generative-ai) | Coursera, Google Cloud\n* [Introduction to Responsible Machine Learning](https://jphall663.github.io/GWU_rml/)\n* [Machine Learning Fairness by Google](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)\n* [Prompt Engineering for ChatGPT](https://www.coursera.org/learn/prompt-engineering) | Coursera, Vanderbilt University\n* [Tech & Ethics Curricula](https://docs.google.com/spreadsheets/d/1Z0DqQeZ-Aeq6LmD17J5m8zeeIR6ywWnH-WO-jWtXE9M/edit#gid=0)\n* [Trustworthy Deep Learning](https://berkeley-deep-learning.github.io/cs294-131-s19/)\n* [Visualizing A Neural Machine Translation Model - Mechanics of Seq2seq Models With Attention](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) | Jay Alammar  \n\n#### Course Syllabi\n\n* [AI Gov & Nat'l Policy '25](https://docs.google.com/document/d/1TDKnykyck_Zq7CNJ3rwwkjipaULxA0iyilD3dVQuUs4/edit?tab=t.0) | Colin Shea-Blymyer's syllabus\n\n### Podcasts and Channels\n\nThis section features podcasts and channels (such as on YouTube) that offer insightful commentary and explanations on responsible AI and machine learning interpretability.\n\n* [Internet of Bugs](https://www.youtube.com/@InternetOfBugs/videos)\n* [Tech Won't Save Us](https://techwontsave.us/)\n* [This Is Technology Ethics: An Introduction](https://technologyethicspod.wordpress.com/)\n\n## AI Incidents, Critiques, and Research Resources\n\n### AI Incident Information Sharing Resources\n\nThis section houses initiatives, networks, repositories, and publications that facilitate collective and interdisciplinary efforts to enhance AI safety. It includes platforms where experts and practitioners come together to share insights, identify potential vulnerabilities, and collaborate on developing robust safeguards for AI systems, including AI incident trackers.\n\n* [AI Incident Database](https://incidentdatabase.ai/) | Responsible AI Collaborative\n* [AI Vulnerability Database](https://avidml.org/) | (AVID)\n* [AIAAIC](https://www.aiaaic.org/)\n* [AI Badness: An open catalog of generative AI badness](https://badness.ai/)\n* [AI Risk Database](https://airisk.io/)\n* [Atlas of AI Risks](https://social-dynamics.net/atlas/)\n* [Brennan Center for Justice, Artificial Intelligence Legislation Tracker](https://www.brennancenter.org/our-work/research-reports/artificial-intelligence-legislation-tracker)\n* [EthicalTech@GW, Deepfakes & Democracy Initiative](https://blogs.gwu.edu/law-eti/deepfakes-disinformation-democracy/)\n* [George Washington University Law School's AI Litigation Database](https://blogs.gwu.edu/law-eti/ai-litigation-database/)\n* [Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database](https://osf.io/fvqg3/)\n* [Mitre's AI Risk Database](https://github.com/mitre-atlas/ai-risk-database) | ![](https://img.shields.io/github/stars/mitre-atlas/ai-risk-database?style=social)\n* [OECD AI Incidents Monitor](https://oecd.ai/en/incidents)\n* [Resemble.AI Deepfake Incident Database](https://www.resemble.ai/deepfake-database/)\n* [Verica Open Incident Database](https://www.thevoid.community/) | (VOID)\n\n#### Bibliography of Papers on AI Incidents and Failures\n\n* [A comprehensive taxonomy of hallucinations in Large Language Models](https://arxiv.org/pdf/2508.01781)\n* [AI Ethics Issues in Real World: Evidence from AI Incident Database](https://doi.org/10.48550/arXiv.2206.07635)\n* [Artificial Intelligence Incidents & Ethics: A Narrative Review](https://doi.org/10.54489/ijtim.v2i2.80)\n* [Artificial Intelligence Safety and Cybersecurity: A Timeline of AI Failures](https://doi.org/10.48550/arXiv.1610.07997)\n* [Center for Countering Digital Hate, YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf) | (CCDH)\n* [Deepfake Pornography Goes to Washington: Measuring the Prevalence of AI-Generated Non-Consensual Intimate Imagery Targeting Congress](https://static1.squarespace.com/static/6612cbdfd9a9ce56ef931004/t/67586997eaec5c6ae3bb5e24/1733847451191/ASP+DFP+Report.pdf) | American Sunlight Project, December 11, 2024\n* [Deployment Corrections: An Incident Response Framework for Frontier AI Models](https://doi.org/10.48550/arXiv.2310.00328)\n* [Exploring Trust With the AI Incident Database](https://doi.org/10.1177/21695067231198084)\n* [Indexing AI Risks with Incidents, Issues, and Variants](https://doi.org/10.48550/arXiv.2211.10384)\n* [Good Systems, Bad Data?: Interpretations of AI Hype and Failures](https://doi.org/10.1002/pra2.275)\n* [Hidden Risks: Artificial Intelligence and Hermeneutic Harm](https://link.springer.com/article/10.1007/s11023-025-09733-0)\n* [How Does AI Fail Us? A Typological Theorization of AI Failures](https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/25/)\n* [New Noodlophile Stealer Distributes Via Fake AI Video Generation Platforms](https://engage.morphisec.com/hubfs/Noodlophile_Ransomware_ThreatAnalysis.pdf) | Morphisec Threat Analysis\n* [Omission and Commission Errors Underlying AI Failures](https://doi.org/10.1007/s00146-022-01585-x)\n* [Ontologies for Reasoning about Failures in AI Systems](https://mclumd.github.io/ALMECOM%20Papers/2007/Schmill%20et%20al.%20-%202007%20-%20Ontologies%20for%20reasoning%20about%20failures%20in%20AI%20syst.pdf)\n* [Planning for Natural Language Failures with the AI Playbook](https://doi.org/10.1145/3411764.3445735)\n* [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://arxiv.org/abs/2011.08512)\n* [SoK: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security](https://doi.org/10.1145/3664476.3664510)\n* [The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone Mobile](https://doi.org/10.48550/arXiv.2407.15685)\n* [Understanding and Avoiding AI Failures: A Practical Guide](https://doi.org/10.3390/philosophies6030053)\n* [When Your AI Becomes a Target: AI Security Incidents and Best Practices](https://doi.org/10.1609/aaai.v38i21.30347)\n* [Why We Need to Know More: Exploring the State of AI Incident Documentation Practices](https://dl.acm.org/doi/fullHtml/10.1145/3600211.3604700)\n\n### AI Law, Policy, and Guidance Trackers\n\nThis section contains trackers, databases, and repositories of laws, policies, and guidance pertaining to AI.\n\n* [AI Governance and Regulatory Archive](https://agora.eto.tech/?) | Emerging Technology Observatory, ETO AGORA\n* [Artificial Intelligence  Policy Collection](https://digital.library.unt.edu/explore/collections/AIPC/) | University of North Texas\n* [Ethical AI Standards in Chile: Responsible and Transparent Algorithms](https://goblab.uai.cl/en/ethical-algorithms/) | GobLab UAI\n* [George Washington University Law School's AI Litigation Database](https://blogs.gwu.edu/law-eti/ai-litigation-database/)\n* [Global AI Governance Tracker](https://vidhisharmaai.com/global-ai-governance-tracker/) | VidhiSharma.AI\n* [Global AI Regulation Tracker](https://www.runwaystrategies.co/global-ai-regulation-tracker) | Runway Strategies\n* [Global AI Regulation Tracker](https://www.techieray.com/GlobalAIRegulationTracker) | Raymond Sun\n* [Global regulatory tracker - United States](https://www.whitecase.com/insight-our-thinking/ai-watch-global-regulatory-tracker-united-states) | White & Case, AI Watch\n* International Association of Privacy Professionals (IAPP)\n  * [Global AI Legislation Tracker](https://iapp.org/resources/article/global-ai-legislation-tracker/)\n  * [UK data protection reform: An overview](https://iapp.org/resources/article/uk-data-protection-reform-an-overview/)\n  * [US State Privacy Legislation Tracker](https://iapp.org/resources/article/us-state-privacy-legislation-tracker/)\n* [Legal Nodes, Global AI Regulations Tracker: Europe, Americas & Asia-Pacific Overview](https://legalnodes.com/article/global-ai-regulations-tracker)\n* [MIT AI Risk Repository](https://airisk.mit.edu/)\n* [multistate.ai](https://www.multistate.ai/)\n* [National AI policies & strategies](https://oecd.ai/en/dashboards/overview) | OECD.AI\n* [National Conference of State Legislatures, Deceptive Audio or Visual Media ‘Deepfakes’ 2024 Legislation](https://www.ncsl.org/technology-and-communication/deceptive-audio-or-visual-media-deepfakes-2024-legislation)\n* [Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report](https://www.accessnow.org/wp-content/uploads/2024/07/TRF-LAC-Reporte-Regional-IA-JUN-2024-V3.pdf) | Access Now\n* [The Ethical AI Database](https://www.eaidb.org/)\n* [Tracking international legislation relevant to AI at work](https://www.ifow.org/publications/legislation-tracker) | Institute for the Future of Work\n\n### Challenges and Competitions\n\nThis section contains challenges and competitions related to responsible ML.\n\n* [FICO Explainable Machine Learning Challenge](https://community.fico.com/s/explainable-machine-learning-challenge)\n* [OSD Bias Bounty](https://osdbiasbounty.com/)\n* [National Fair Housing Alliance Hackathon](https://nationalfairhousing.org/hackathon2023/)\n* [Twitter Algorithmic Bias](https://hackerone.com/twitter-algorithmic-bias?type=team)\n\n### AI and Labor Resources\n\nThis section contains links to papers, studies, and general resources pertaining to the relationship between AI and labor dynamics.\n\n* [AI and Jobs: Evidence from Online Vacancies](https://www.nber.org/system/files/working_papers/w28257/w28257.pdf) | National Bureau of Economic Research, NBER Working Paper Series, Working Paper 28257, December 2020, Revised February 2022\n* [AI and the Economy](https://www.nber.org/system/files/working_papers/w24689/w24689.pdf) | National Bureau of Economic Research, NBER Working Paper Series, Working Paper 24689, June 2018\n* [AI, Labor, Productivity and the Need for Firm-Level Data](https://www.nber.org/system/files/chapters/c14037/revisions/c14037.rev0.pdf) | Manav Raj and Robert Seamans, NYU Stern School of Business, December 12, 2017 draft\n* [Artificial Intelligence and Labor Market Transformations in Latin America](https://docs.iza.org/dp17746.pdf) | IZA Institute of Labor Economics, IZA DP No. 17746, February 2025\n* [Artificial Intelligence and the Changing Demand for Skills in the Labour Market](https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/04/artificial-intelligence-and-the-changing-demand-for-skills-in-the-labour-market_861a23ea/88684e36-en.pdf) | OECD Artificial Intelligence Papers, No. 14, April 2024\n* [Artificial Intelligence and the Labor Market](https://www.nber.org/system/files/working_papers/w33509/w33509.pdf) | National Bureau of Economic Research, NBER Working Paper Series, Working Paper 33509, February 2025, Revised September 2025\n* [Artificial Intelligence And Worker Well-being: Principles And Best Practices For Developers And Employers](https://data.aclum.org/storage/2025/01/DOL_www_dol_gov_general_AI-Principles.pdf) | United States Department of Labor\n* [Artificial Intelligence Impact on Labor Markets: Literature Review](https://www.iedconline.org/clientuploads/EDRP%20Logos/AI_Impact_on_Labor_Markets.pdf) | Economic Development Research Partners, International Economic Development Council\n* [Artificial Intelligence, Automation and Work](https://www.nber.org/system/files/working_papers/w24196/w24196.pdf) | National Bureau of Economic Research, NBER Working Paper Series, Working Paper 24196, January 2018\n* [Belaboring the Algorithm: Artificial Intelligence and Labor Unions](https://www.yalejreg.com/wp-content/uploads/Kelley.Bulletin.pdf) | Bradford J. Kelley\n* [Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence](https://digitaleconomy.stanford.edu/wp-content/uploads/2025/08/Canaries_BrynjolfssonChandarChen.pdf) | August 26, 2025\n* [Displacement or Complementarity? The Labor Market Impact of Generative AI](https://www.hbs.edu/ris/Publication%20Files/25-039_05fbec84-1f23-459b-8410-e3cd7ab6c88a.pdf) | Harvard Business School, Working Paper 25-039, 2025\n* [Estimating AI productivity gains from Claude conversations](https://www-cdn.anthropic.com/e5645986a7ce8fbcc48fa6d2fc67753c87642c30.pdf) | Alex Tamkin and Peter McCrory, November 5, 2025\n* [Evaluating the Impact of AI on the Labor Market: Current State of Affairs](https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs) | Yale Budget Lab, October 1, 2025\n* [Future of Jobs Report 2025](https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf) | World Economic Forum, Insight Report, January 2025\n* [Generative AI and jobs: A global analysis of potential effects on job quantity and quality](https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@inst/documents/publication/wcms_890761.pdf) | International Labour Organization, ILO Working Paper 96, August 2023\n* [Generative AI and Labor: Power, Hype, and Value at Work](https://datasociety.net/wp-content/uploads/2024/12/DS_Generative-AI-and-Labor-Primer_Final.pdf) | Data & Society, December 2024\n* [Generative AI and the future of work in America](https://www.mckinsey.com/~/media/mckinsey/mckinsey%20global%20institute/our%20research/generative%20ai%20and%20the%20future%20of%20work%20in%20america/generative-ai-and-the-future-of-work-in-america-vf1.pdf) | McKinsey Global Institute, July 2023\n* [Impact and regulations of AI on labor markets and employment in USA](https://ijsra.net/sites/default/files/IJSRA-2024-1670.pdf) | September 9, 2024\n* [Impacts of generative artificial intelligence on the future of labor market: A systematic review](https://www.sciencedirect.com/science/article/pii/S2451958825000673) | May 2025\n* [Job Transformation, Specialization, and the Labor Market Effects of AI](https://lukasbfreund.github.io/files/FM_AI.pdf) | Lukas B. Freund and Lukas F. Mann, August 16, 2025, Revised November 19, 2025\n* [Labor in the Age of Automation and Artificial Intelligence](https://econfip.org/wp-content/uploads/2019/02/6.Labor-in-the-Age-of-Automation-and-Artificial-Intelligence.pdf) | Anton Korinek, Economists for Inclusive Prosperity, Research Brief, January 2019\n* [Labor Market Exposure to AI: Cross-country Differences and Distributional Implications](https://www.imf.org/en/-/media/files/publications/wp/2023/english/wpiea2023216-print-pdf.pdf) | International Monetary Fund, WP/23/216, October 2023\n* [Making Talk Cheap: Generative AI and Labor Market Signaling](https://jesse-silbert.github.io/website/silbert_jmp.pdf) | November 14, 2025\n* [Potential Labor Market Impacts of Artificial Intelligence: An Empirical Analysis](https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/07/Potential-Labor-Market-Impacts-of-Artificial-Intelligence-An-Empirical-Analysis-July-2024.pdf) | Biden White House Archives, July 2024\n* [Technological Disruption in the US Labor Market](https://www.economicstrategygroup.org/wp-content/uploads/2024/10/Deming-Ong-Summers-AESG-2024.pdf) | Aspen Economic Strategy Group, 2024\n* [The Iceberg Index: Measuring Skills-centered Exposure in the AI Economy](https://iceberg.mit.edu/report.pdf) | Project Iceberg\n* [The Impact of Artificial Intelligence on Employment](https://www.bruegel.org/sites/default/files/wp-content/uploads/2018/07/Impact-of-AI-Petroupoulos.pdf) | Georgios Petropoulos, July 2018\n* [The Impact of Artificial Intelligence on the Labor Market](https://www.michaelwebb.co/webb_ai.pdf) | Michael Webb, Stanford University, January 2020\n* [The Impact of Artificial Intelligence on Work: An evidence review prepared for the Royal Society and the British Academy](https://www.frontier-economics.com/media/q4lnsyv1/ai-and-work-evidence-review-full-report.pdf) | Frontier Economics, September 2018\n* [The Labor Market Effects of Generative AI: A Difference-in-Differences Analysis of AI Exposure](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5375017) | SSRN, Andrew C. Johnston and Christos Makridis, October 30, 2025\n* [The Labor Market Impact of Artificial Intelligence: Evidence from US Regions](https://www.elibrary.imf.org/downloadpdf/view/journals/001/2024/199/001.2024.issue-199-en.pdf) | International Monetary Fund, Yueling Huang, WP/24/199, September 2024\n* [Toward understanding the impact of artificial intelligence on labor](https://estebanmoro.org/pdf/Toward_understanding_the_impact_of_artificial_intelligence_on_labor.pdf) | PNAS, February 28, 2019\n* [Workforce Intelligence: From MIT experts, strategies to transform skills, roles, and human potential across your organization](https://mitsloan.mit.edu/sites/default/files/2025-09/MIT%20Sloan%20-%20Workforce%20Intelligence-digital.pdf) | MIT Sloan School of Management\n\n### Responsible and Critical Perspectives on Agentic AI\n\nThis section collects papers on agentic AI with a focus on safety, governance, responsible use, and critique. It includes technical, social, legal, and policy-oriented perspectives on the risks, evaluation, and oversight of AI agents.\n\n* [A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents](https://arxiv.org/pdf/2504.14650.pdf)\n* [A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?](https://arxiv.org/pdf/2505.10924.pdf)\n* [A Systematization of Security Vulnerabilities in Computer Use Agents](https://arxiv.org/pdf/2507.05445.pdf)\n* [AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents](https://arxiv.org/pdf/2506.00641.pdf)\n* [AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents](https://arxiv.org/pdf/2406.13352.pdf)\n* [AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents](https://openreview.net/pdf?id=AC5n7xHuR1)\n* [Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges](https://arxiv.org/pdf/2510.23883.pdf)\n* [Agentic AI: Autonomy, Accountability, and the Algorithmic Society](https://arxiv.org/pdf/2502.00289.pdf)\n* [Agents of Chaos](https://arxiv.org/pdf/2602.20021)\n* [Agent-SafetyBench: Evaluating the Safety of LLM Agents](https://arxiv.org/pdf/2412.14470.pdf)\n* [AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification](https://arxiv.org/pdf/2602.22724.pdf)\n* [AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways](https://arxiv.org/pdf/2406.02630.pdf)\n* [Decentralized Governance of Autonomous AI Agents](https://arxiv.org/pdf/2412.17114.pdf)\n* [Design Patterns for Securing LLM Agents against Prompt Injections](https://arxiv.org/pdf/2506.08837.pdf)\n* [Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System](https://arxiv.org/pdf/2502.07254.pdf)\n* [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://arxiv.org/pdf/2404.08144.pdf)\n* [Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree](https://arxiv.org/pdf/2507.14799.pdf)\n* [MI9: An Integrated Runtime Governance Framework for Agentic AI](https://arxiv.org/pdf/2508.03858.pdf)\n* [Practices for Governing Agentic AI Systems](https://cdn.openai.com/papers/practices-for-governing-agentic-ai-systems.pdf)\n* [Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment](https://arxiv.org/pdf/2406.04231.pdf)\n* [RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments](https://arxiv.org/pdf/2506.15253.pdf)\n* [R-Judge: Benchmarking Safety Risk Awareness for LLM Agents](https://arxiv.org/pdf/2401.10019.pdf)\n* [SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents](https://arxiv.org/pdf/2412.13178.pdf)\n* [SAGA: A Security Architecture for Governing AI Agentic Systems](https://arxiv.org/pdf/2504.21034.pdf)\n* [Silent Egress: When Implicit Prompt Injection Makes LLM Agents Leak Without a Trace](https://arxiv.org/pdf/2602.22450.pdf)\n* [Teams of LLM Agents can Exploit Zero-Day Vulnerabilities](https://arxiv.org/pdf/2406.01637.pdf)\n* [The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process](https://arxiv.org/pdf/2506.01080.pdf)\n* [The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis](https://arxiv.org/pdf/2602.10453.pdf)\n* [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://arxiv.org/pdf/2506.04133.pdf)\n* [Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation](https://arxiv.org/pdf/2508.14031.pdf)\n* [With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems](https://arxiv.org/pdf/2512.22211.pdf)\n\n\n### Critiques of AI\n\nThis section contains an assortment of papers, articles, essays, and general resources that take critical stances toward generative AI.\n\n* [Against predictive optimization](https://predictive-optimization.cs.princeton.edu/)\n* [AI as Normal Technology](https://knightcolumbia.org/content/ai-as-normal-technology) | Arvind Narayanan and Sayash Kapoor, April 15, 2025\n* [AI Bias is Not Ideological. It's Science.](https://www.techpolicy.press/ai-bias-is-not-ideological-its-science/)\n* [AI can only do 5% of jobs, says MIT economist who fears tech stock crash](https://torontosun.com/business/money-news/ai-can-only-do-5-of-jobs-says-mit-economist-who-fears-tech-stock-crash)\n* [AI chatbots use racist stereotypes even after anti-racism training](https://www.newscientist.com/article/2421067-ai-chatbots-use-racist-stereotypes-even-after-anti-racism-training/)\n* [AI coding assistants do not boost productivity or prevent burnout, study finds](https://www.techspot.com/news/104945-ai-coding-assistants-do-not-boost-productivity-or.html)\n* [AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business](https://link.springer.com/article/10.1007/s43681-024-00443-4)\n* [AI hype, promotional culture, and affective capitalism](https://link.springer.com/article/10.1007/s43681-024-00483-w)\n* [AI Is a Lot of Work](https://nymag.com/intelligencer/article/ai-artificial-intelligence-humans-technology-business-factory.html)\n* [AI is effectively ‘useless’—and it’s created a ‘fake it till you make it’ bubble that could end in disaster, veteran market watcher warns](https://finance.yahoo.com/news/ai-effectively-useless-created-fake-194008129.html)\n* [AI Safety Is a Narrative Problem](https://hdsr.mitpress.mit.edu/pub/wz35dvpo/release/1?readingCollection=3974b7e6)\n* [AI Snake Oil](https://www.aisnakeoil.com/)\n* [AI Tools Still Permitting Political Disinfo Creation, NGO Warns](https://www.barrons.com/news/ai-tools-still-permitting-political-disinfo-creation-ngo-warns-ac791521)\n* [Anthropomorphism in AI: hype and fallacy](https://link.springer.com/article/10.1007/s43681-024-00419-4)\n* [Are Emergent Abilities of Large Language Models a Mirage?](https://arxiv.org/pdf/2304.15004.pdf)\n* [Are Language Models Actually Useful for Time Series Forecasting?](https://arxiv.org/abs/2406.16964v1)\n* [Artificial Hallucinations in ChatGPT: Implications in Scientific Writing](https://assets.cureus.com/uploads/editorial/pdf/138667/20230219-28928-6kcyip.pdf)\n* [Artificial Hype](https://egve.hu/downloads/health_management/health_management_2019_2_szam.pdf) | HealthManagement.org, The Journal, Volume 19, Issue 2, 2019\n* [Artificial intelligence and illusions of understanding in scientific research](https://rdcu.be/dAw4I)\n* [Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients](https://qualitysafety.bmj.com/content/early/2024/09/18/bmjqs-2024-017476)\n* [Artificial Intelligence: Hope for Future or Hype by Intellectuals?](https://ieeexplore.ieee.org/abstract/document/9596410)\n* [ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs](https://arxiv.org/pdf/2402.11753.pdf)\n* [Authoritarian by Design: AI, Big Tech, and the Architecture of Control](https://thegoodtechproject.addpotion.com/authoritarian-by-design-ai-big-tech-and-the-architecture-of-control)\n* [Aylin Caliskan's publications](https://faculty.washington.edu/aylin/publications.html)\n* [Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks](https://arxiv.org/abs/2407.21072)\n* [Beyond Preferences in AI Alignment](https://arxiv.org/pdf/2408.16984)\n* [Can We Trust AI Agents? An Experimental Study Towards Trustworthy LLM-Based Multi-Agent Systems for AI Ethics](https://arxiv.org/abs/2411.08881)\n* [Chatbots in consumer finance](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/)\n* [ChatGPT is bullshit](https://link.springer.com/article/10.1007/s10676-024-09775-5)\n* [Companies like Google and OpenAI are pillaging the internet and pretending it’s progress](https://bgr.com/business/companies-like-google-and-openai-are-pillaging-the-internet-and-pretending-its-progress/)\n* [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://arxiv.org/abs/2308.08708)\n* [Data and its discontents: A survey of dataset development and use in machine learning research](https://tinyurl.com/2rx43mrz)\n* [Does current AI represent a dead end?](https://www.bcs.org/articles-opinion-and-research/does-current-ai-represent-a-dead-end/) | BCS\n* [Ed Zitron's Where's Your Ed At](https://www.wheresyoured.at/)\n* [Emergent and Predictable Memorization in Large Language Models](https://arxiv.org/abs/2304.11158)\n* [Evaluating Language-Model Agents on Realistic Autonomous Tasks](https://arxiv.org/pdf/2312.11671.pdf)\n* [Explainable AI: The What’s and Why’s, Part 1: The What](https://ryanallen42.medium.com/explainable-ai-the-whats-and-why-s-175ea344bf3a) | Ryan Allen\n* [FABLES: Evaluating faithfulness and content selection in book-length summarization](https://arxiv.org/abs/2404.01261)\n* [Futurism, Disillusioned Businesses Discovering That AI Kind of Sucks](https://futurism.com/the-byte/businesses-discovering-ai-sucks)\n* [Gen AI: Too Much Spend, Too Little Benefit?](https://www.goldmansachs.com/intelligence/pages/gs-research/gen-ai-too-much-spend-too-little-benefit/report.pdf)\n* [Generative AI: UNESCO study reveals alarming evidence of regressive gender stereotypes](https://www.unesco.org/en/articles/generative-ai-unesco-study-reveals-alarming-evidence-regressive-gender-stereotypes)\n* [Get Ready for the Great AI Disappointment](https://www.wired.com/story/get-ready-for-the-great-ai-disappointment/)\n* [Ghost in the Cloud: Transhumanism’s simulation theology](https://www.nplusonemag.com/issue-28/essays/ghost-in-the-cloud/)\n* [Handling the hype: Implications of AI hype for public interest tech projects](https://www.tatup.de/index.php/tatup/article/view/7080)\n* [How AI hype impacts the LGBTQ + community](https://link.springer.com/article/10.1007/s43681-024-00423-8)\n* [How AI lies, cheats, and grovels to succeed - and what we need to do about it](https://www.zdnet.com/article/how-ai-lies-cheats-and-grovels-to-succeed-and-what-we-need-to-do-about-it/)\n* [How to Tell if Something is AI-Written](https://hollisrobbinsanecdotal.substack.com/p/how-to-tell-if-something-is-ai-written) | Anecdotal Value, Hollis Robbins, August 12, 2025\n* [I Will Fucking Piledrive You If You Mention AI Again](https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/)\n* [Identifying and Eliminating CSAM in Generative ML Training Data and Models](https://stacks.stanford.edu/file/druid:kh752sm9123/ml_training_data_csam_report-2023-12-23.pdf)\n* [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf)\n* [Insanely Complicated, Hopelessly Inadequate](https://www.lrb.co.uk/the-paper/v43/n02/paul-taylor/insanely-complicated-hopelessly-inadequate)\n* [Internet of Bugs, Debunking Devin: \"First AI Software Engineer\" Upwork lie exposed!](https://www.youtube.com/watch?v=tNmgmwEtoWE)\n* [It’s Time to Stop Taking Sam Altman at His Word](https://www.theatlantic.com/technology/archive/2024/10/sam-altman-mythmaking/680152/)\n* [Large Language Models are Unreliable for Cyber Threat Intelligence](https://arxiv.org/pdf/2503.23175)\n* [Large Language Models Do Not Simulate Human Psychology](https://arxiv.org/pdf/2508.06950)\n* [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://arxiv.org/abs/2401.01301)\n* [Lazy use of AI leads to Amazon products called “I cannot fulfill that request”](https://arstechnica.com/ai/2024/01/lazy-use-of-ai-leads-to-amazon-products-called-i-cannot-fulfill-that-request/)\n* [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://arxiv.org/pdf/2402.03927.pdf)\n* [LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks](https://arxiv.org/pdf/2402.01817.pdf)\n* [Long-context LLMs Struggle with Long In-context Learning](https://huggingface.co/papers/2404.02060)\n* [Low-Resource Languages Jailbreak GPT-4](https://arxiv.org/abs/2310.02446v1)\n* [Machine Learning: The High Interest Credit Card of Technical Debt](https://research.google/pubs/machine-learning-the-high-interest-credit-card-of-technical-debt/)\n* [Measuring the predictability of life outcomes with a scientific mass collaboration](https://www.pnas.org/doi/10.1073/pnas.1915006117)\n* [Medical large language models are vulnerable to data-poisoning attacks](https://www.nature.com/articles/s41591-024-03445-1)\n* [Meta AI Chief: Large Language Models Won't Achieve AGI](https://www.msn.com/en-us/news/technology/meta-ai-chief-large-language-models-won-t-achieve-agi/ar-BB1mRPa5)\n* [Meta’s AI chief: LLMs will never reach human-level intelligence](https://thenextweb.com/news/meta-yann-lecun-ai-behind-human-intelligence)\n* [MIT Technology Review, Introducing: The AI Hype Index](https://www.technologyreview.com/2024/10/23/1105192/ai-hype-index-nov-dec-2024/)\n* [Most CEOs aren’t buying the hype on generative AI benefits](https://www.itpro.com/business/leadership/most-ceos-arent-buying-the-hype-on-generative-ai-benefits)\n* [The Most Dangerous Fiction: The Rhetoric and Reality of the AI Race](https://dx.doi.org/10.2139/ssrn.5278644)\n* [Nepotistically Trained Generative-AI Models Collapse](https://arxiv.org/abs/2311.12202)\n* [Non-discrimination Criteria for Generative Language Models](https://arxiv.org/abs/2403.08564)\n* [On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)\n* [On the Very Real Dangers of the Artificial Intelligence Hype Machine](https://lithub.com/on-the-very-real-dangers-of-the-artificial-intelligence-hype-machine/)\n* [OpenAI—written evidence, House of Lords Communications and Digital Select Committee inquiry: Large language models](https://committees.parliament.uk/writtenevidence/126981/pdf/) | (LLM0113)\n  * [Former OpenAI Researcher Says the Company Broke Copyright Law](https://www.nytimes.com/2024/10/23/technology/openai-copyright-law.html)\n* [Open Problems in Technical AI Governance](https://arxiv.org/pdf/2407.14981)\n* [Pivot to AI](https://pivot-to-ai.com/)\n* [Press Pause on the Silicon Valley Hype Machine](https://www.nytimes.com/2024/05/15/opinion/artificial-intelligence-ai-openai-chatgpt-overrated-hype.html) | Julia Angwin\n* [Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models](https://arxiv.org/pdf/2311.00871.pdf)\n* [Prohibiting Generative AI in any Form of Weapon Control](https://openreview.net/pdf/1360a037fb677e6a6eebdf1f618cee43be081cc1.pdf)\n* [Promising the future, encoding the past: AI hype and public media imagery](https://link.springer.com/article/10.1007/s43681-024-00474-x)\n* [Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad](https://arxiv.org/pdf/2503.21934v1) | arXiv, March 2025\n* [Quantifying Memorization Across Neural Language Models](https://arxiv.org/abs/2202.07646)\n* [Re-evaluating GPT-4’s bar exam performance](https://link.springer.com/article/10.1007/s10506-024-09396-9)\n* [Researchers surprised by gender stereotypes in ChatGPT](https://www.dtu.dk/english/news/all-news/researchers-surprised-by-gender-stereotypes-in-chatgpt?id=7e5936d1-dfce-485b-8a90-78f7c757177d)\n* [Sam Altman’s imperial reach](https://www.washingtonpost.com/opinions/2024/10/07/sam-altman-ai-power-danger/)\n* [Scalable Extraction of Training Data from Production Language Models](https://arxiv.org/pdf/2311.17035.pdf)\n* [Speed of AI development stretches risk assessments to breaking point](https://www.ft.com/content/499c8935-f46e-4ec8-a8e2-19e07e3b0438)\n* [Talking existential risk into being: a Habermasian critical discourse perspective to AI hype](https://link.springer.com/article/10.1007/s43681-024-00464-z)\n* [Task Contamination: Language Models May Not Be Few-Shot Anymore](https://arxiv.org/pdf/2312.16337.pdf)\n* [The Cult of AI](https://www.rollingstone.com/culture/culture-features/ai-companies-advocates-cult-1234954528/)\n* [The Data Scientific Method vs. The Scientific Method](https://odsc.com/blog/the-data-scientific-method-vs-the-scientific-method/)\n* [The Fallacy of AI Functionality](https://dl.acm.org/doi/pdf/10.1145/3531146.3533158)\n* [The harms of terminology: why we should reject so-called “frontier AI”](https://link.springer.com/article/10.1007/s43681-024-00438-1)\n* [The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’](https://www.nature.com/articles/s41587-023-02103-0)\n* [The Price of Emotion: Privacy, Manipulation, and Bias in Emotional AI](https://www.americanbar.org/groups/business_law/resources/business-law-today/2024-september/price-emotion-privacy-manipulation-bias-emotional-ai/)\n* [Theory Is All You Need: AI, Human Cognition, and Decision Making](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4737265)\n* [There Is No A.I.](https://www.newyorker.com/science/annals-of-artificial-intelligence/there-is-no-ai)\n* [There’s Nothing Magical in the Machine](https://www.nytimes.com/2025/09/25/opinion/artificial-intelligence-magical-thinking.html)\n* [This AI Pioneer Thinks AI Is Dumber Than a Cat](https://www.wsj.com/tech/ai/yann-lecun-ai-meta-aa59e2f5)\n* [Three different types of AI hype in healthcare](https://link.springer.com/article/10.1007/s43681-024-00465-y)\n* [Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context](https://link.springer.com/article/10.1007/s11023-024-09668-y)\n* [We still don't know what generative AI is good for](https://www.msn.com/en-us/news/technology/we-still-dont-know-what-generative-ai-is-good-for/ar-AA1nz1QH)\n* [What’s in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT](https://www.jmir.org/2024/1/e51837/)\n* [Which Humans?](https://osf.io/preprints/psyarxiv/5b26t)\n* [Why the AI Hype is Another Tech Bubble](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4960826)\n* [Why We Must Resist AI’s Soft Mind Control]( https://www.theatlantic.com/ideas/archive/2024/03/artificial-intelligence-google-gemini-mind-control/677683/)\n* [Winner's Curse? On Pace, Progress, and Empirical Rigor](https://openreview.net/pdf?id=rJWF0Fywf)\n* [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://arxiv.org/pdf/2506.08872v1)\n* [YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf) | Center for Countering Digital Hate (CCDH),\n\n#### Environmental Costs of AI\n\n* [A bottle of water per email: the hidden environmental costs of using AI chatbots](https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/)\n* [AI already uses as much energy as a small country. It’s only the beginning.](https://www.vox.com/climate/2024/3/28/24111721/ai-uses-a-lot-of-energy-experts-expect-it-to-double-in-just-a-few-years)\n* [AI, Climate, and Regulation: From Data Centers to the AI Act](https://arxiv.org/abs/2410.06681)\n* [AI for a Planet Under Pressure](https://www.stockholmresilience.org/download/18.15c171219a15332ff93f68/1762500974262/AI%20for%20a%20planet%20under%20pressure_digital2.pdf) | Stockholm Resilience Centre, Stockholm University, November 5, 2025\n* [Artificial Intelligence and Environmental Impact: Moving Beyond Humanizing Vocabulary and Anthropocentrism](https://www.liebertpub.com/doi/abs/10.1089/omi.2024.0197)\n* [Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning's carbon footprint](https://doi.org/10.1177/25148486251332087)\n* [Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models](https://dl.acm.org/doi/abs/10.1145/3578337.3605121)\n* [The Climate and Sustainability Implications of Generative AI](https://mit-genai.pubpub.org/pub/8ulgrckc/release/2)\n* [Data centre water consumption](https://www.nature.com/articles/s41545-021-00101-w)\n* [Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence ](https://www.nature.com/articles/s41599-024-03520-5.pdf)\n* [Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI](https://arxiv.org/abs/2309.02065)\n* [Ensuring a carbon-neutral future for artificial intelligence](https://www.the-innovation.org/data/article/energy/preview/pdf/XINNENERGY-2024-0095.pdf)\n* [Environment and sustainability development: A ChatGPT perspective](https://www.taylorfrancis.com/chapters/oa-edit/10.1201/9781003471059-8/environment-sustainability-development-chatgpt-perspective-priyanka-bhaskar-neha-seth)\n* [Generative AI’s environmental costs are soaring — and mostly secret](https://www.nature.com/articles/d41586-024-00478-x)\n* [Green Intelligence Resource Hub](https://docs.google.com/spreadsheets/d/1UCsgAqgonjpP9uPVyssXU0VE0G6Fs7ydxt_mmrpcd1o/edit?usp=sharing)\n* [Making AI Less \"Thirsty\": Uncovering and Addressing the Secret Water Footprint of AI Models](https://arxiv.org/abs/2304.03271)\n* [Measuring the Environmental Impact of Delivering AI at Google Scale](https://arxiv.org/pdf/2508.15734)\n* [Measuring the environmental impacts of artificial intelligence compute and applications](https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html) | OECD\n* [Microsoft’s Hypocrisy on AI](https://www.theatlantic.com/technology/archive/2024/09/microsoft-ai-oil-contracts/679804/)\n* [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://dl.acm.org/doi/pdf/10.1145/3630106.3658542)\n* [Powering artificial intelligence: A study of AI's environmental footprint—today and tomorrow, November 2024](https://www.deloitte.com/content/dam/assets-shared/docs/about/2024/powering-artificial-intelligence.pdf) | Deloitte\n* [Promoting Sustainability: Mitigating the Water Footprint in AI-Embedded Data Centres](https://www.igi-global.com/chapter/promoting-sustainability/341617)\n* [Sustainable AI: AI for sustainability and the sustainability of AI](https://link.springer.com/article/10.1007/s43681-021-00043-6)\n* [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://proceedings.mlsys.org/paper_files/paper/2022/file/462211f67c7d858f663355eff93b745e-Paper.pdf)\n* [The AI Carbon Footprint and Responsibilities of AI Scientists](https://www.mdpi.com/2409-9287/7/1/4)\n* [The Carbon Footprint of Artificial Intelligence](https://dl.acm.org/doi/pdf/10.1145/3603746)\n* [The carbon impact of artificial intelligence](https://www.nature.com/articles/s42256-020-0219-9)\n* [The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT](https://puiij.com/index.php/research/article/view/39)\n* [The Environmental Price of Intelligence: Evaluating the Social Cost of Carbon in Machine Learning](https://ieeexplore.ieee.org/abstract/document/10553496)\n* [The growing energy footprint of artificial intelligence](https://www.cell.com/action/showPdf?pii=S2542-4351%2823%2900365-3)\n* [The Hidden Cost of AI: Carbon Footprint and Mitigation Strategies](https://dx.doi.org/10.2139/ssrn.5036344)\n* [The Hidden Cost of AI: Unraveling the Power-Hungry Nature of Large Language Models](https://www.preprints.org/frontend/manuscript/30dc8badac9e44da699113e5b5cd6737/download_pub)\n* [The Hidden Costs of AI-driven Data Center Demand: Five Systemic Tensions](https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1039&context=amcis2025)\n* [The Hidden Environmental Impact of AI](https://jacobin.com/2024/06/ai-data-center-energy-usage-environment/)\n* [The mechanisms of AI hype and its planetary and social costs](https://link.springer.com/article/10.1007/s43681-024-00461-2)\n* [Toward Responsible AI Use: Considerations for Sustainability Impact Assessment](https://arxiv.org/abs/2312.11996)\n* [Towards A Comprehensive Assessment of AI's Environmental Impact](https://arxiv.org/abs/2405.14004)\n* [Towards Environmentally Equitable AI via Geographical Load Balancing](https://arxiv.org/abs/2307.05494)\n* [Towards green and sustainable artificial intelligence: quantifying the energy footprint of logistic regression and decision tree algorithms](https://ieeexplore.ieee.org/abstract/document/10700922)\n* [Tracking the carbon footprint of global generative artificial intelligence](https://www.cell.com/action/showPdf?pii=S2666-6758%2825%2900069-4)\n* [Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions](https://www.mdpi.com/2071-1050/14/9/5172)\n* [We did the math on AI's energy footprint. Here's the story you haven't heard.](https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/) | MIT Technology Review, May 20, 2025\n\n#### Language Diversity and Resource Gaps\n\n* [Health Care Misinformation: An artificial intelligence challenge for low-resource languages](https://ceur-ws.org/Vol-2884/paper_131.pdf)\n* [The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Mali](https://arxiv.org/pdf/2503.03380)\n\n#### AI Slop Genre\n\n* [AI Slop Might Finally Cure Our Internet Addiction](https://www.theatlantic.com/technology/archive/2025/07/ai-slop-internet-addiction/683619/)\n* [Living the Slop Life](https://www.nytimes.com/2025/05/19/style/ai-slop-slop-bowls-shein-slop-hauls.html) | The New York Times, Emma Goldberg, 5/19/2025\n\n#### Measurement Critiques\n\n* [The Leaderboard Illusion](https://arxiv.org/pdf/2504.20879)\n\n### Groups and Organizations\n\n* [Aapti Institute](https://aapti.in/)\n* [Ada Lovelace Institute](https://www.adalovelaceinstitute.org) \n* [AI & Faith](https://aiandfaith.org)\n* [AI Ethics Lab](https://aiethicslab.com)\n* [AI for Good Foundation](https://ai4good.org)\n* [AI Forum New Zealand, AI Governance Working Group](https://aiforum.org.nz/our-work/working-groups/ai-governance-working-group/)\n* [AI Hub for Sustainable Development](https://www.aihubfordevelopment.org/)\n* [AI Now Institute](https://ainowinstitute.org)\n* [AI Policy Exchange](https://aipolicyexchange.org/)\n* [AI Transparency Institute](https://aitransparencyinstitute.com/)\n* [AI Village](https://aivillage.org/)\n* [The Alan Turing Institute](https://www.turing.ac.uk/)\n* [Algorithmic Justice League](https://www.ajl.org/)\n* [Berkman Klein Center for Internet & Society at Harvard University](https://cyber.harvard.edu/)\n* [Center for Advancing Safety of Machine Intelligence](https://casmi.northwestern.edu/)\n* [Center for AI and Digital Policy](https://www.caidp.org)\n* [Center for Democracy and Technology](https://cdt.org/)\n* [Center for Humane Technology](https://www.humanetech.com/)\n* [Center for Security and Emerging Technology](https://cset.georgetown.edu/)\n* [Convergence Analysis](https://www.convergenceanalysis.org/about-us)\n* [Data & Society](https://datasociety.net)\n* [Distributed AI Research Institute](https://www.dair-institute.org) |  (DAIR)\n* [Future of Life Institute](https://futureoflife.org/)\n* [Global Center on AI Governance](https://www.globalcenter.ai/)\n* [Indigenous Protocol and Artificial Intelligence Working Group](https://www.indigenous-ai.net/)\n* [Institute for Advanced Study, AI Policy and Governance Working Group](https://www.ias.edu/stsv-lab/aipolicy)\n* [Institute for Ethics and the Common Good, Notre Dame-IBM Technology Ethics Lab](https://ethics.nd.edu/labs-and-centers/notre-dame-ibm-technology-ethics-lab/)\n* [Leverhulme Centre for the Future of Intelligence](https://lcfi.ac.uk)\n* [Montreal AI Ethics Institute](https://montrealethics.ai)\n* [Partnership on AI](https://partnershiponai.org/)\n* [Responsible Artificial Intelligence Institute](https://responsible.ai)\n* [Stanford University Human-Centered Artificial Intelligence](https://hai.stanford.edu/) |  (HAI)\n* [TheGovLab](https://thegovlab.org/)\n\n### Curated Bibliographies\n\nWe are seeking curated bibliographies related to responsible ML across various topics, see [issue 115](https://github.com/jphall663/awesome-machine-learning-interpretability/issues/115).\n\n* [Artificial Intelligence Policy Supplementary Reading List](https://www.blairaf.com/library/resources/teaching/2024-INF1005H1S/INF1005-Supplementary-Reading-List.pdf) | Blair Attard-Frost, INF1005H1S\n* [Global regulatory tracker - United States](https://www.whitecase.com/insight-our-thinking/ai-watch-global-regulatory-tracker-united-states) | White & Case, AI Watch\n* [Green Intelligence Resource Hub](https://docs.google.com/spreadsheets/d/1UCsgAqgonjpP9uPVyssXU0VE0G6Fs7ydxt_mmrpcd1o/edit?usp=sharing)\n* [LLM Security & Privacy](https://github.com/chawins/llm-sp) | ![](https://img.shields.io/github/stars/chawins/llm-sp?style=social)\n* [Responsible Computing](https://www.internetruleslab.com/responsible-computing) | Internet Rules Lab\n* [Membership Inference Attacks and Defenses on Machine Learning Models Literature](https://github.com/HongshengHu/membership-inference-machine-learning-literature) | ![](https://img.shields.io/github/stars/HongshengHu/membership-inference-machine-learning-literature?style=social)\n\n* **BibTeX**\n  * [A Responsible Machine Learning Workflow](https://github.com/h2oai/article-information-2019/blob/master/back_up/article-information-2019.bib.bak) | ![](https://img.shields.io/github/stars/h2oai/article-information-2019?style=social) | (paper, bibliography)\n  * [Proposed Guidelines for Responsible Use of Explainable Machine Learning](https://github.com/jphall663/kdd_2019/blob/master/bibliography.bib) | ![](https://img.shields.io/github/stars/jphall663/kdd_2019?style=social) |  (presentation, bibliography)\n  * [Proposed Guidelines for Responsible Use of Explainable Machine Learning](https://github.com/jphall663/responsible_xai/blob/master/responsible_xai.bib) | ![](https://img.shields.io/github/stars/jphall663/responsible_xai?style=social) | (paper, bibliography)\n\n* **Web**\n  * [Fairness, Accountability, and Transparency in Machine Learning Scholarship](https://www.fatml.org/resources/relevant-scholarship) | (FAT/ML)\n\n### List of Lists\n\nThis section links to other lists of responsible ML or related resources.\n\n* [2024 AI Resources](https://docs.google.com/document/d/1M--GEa5G4pxMHG5FMeUZKbMtIwAtrWJsBtWZTVGVIqI/edit?tab=t.0) | Chris Kraft\n* [A Living and Curated Collection of Explainable AI Methods](https://utwente-dmb.github.io/xai-papers/#/)\n* [A review of 200 guidelines and recommendations for AI governance](https://doi.org/10.1016/j.patter.2023.100857) | Worldwide AI ethics\n* [AI Ethics & Policy News spreadsheet](https://docs.google.com/spreadsheets/d/11Ps8ILDHH-vojJGyIx7CcaoB5l1mBRHy3OQAgWkm0W4/edit#gid=0) | Casey Fiesler\n* [AI Ethics Guidelines Global Inventory](https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/)\n* [AI Ethics Resources](https://www.fast.ai/posts/2018-09-24-ai-ethics-resources.html)\n* [AI Guidance Resources](https://wde.instructure.com/courses/826) | Wyoming Department of Education (WDE)\n* [AI Tools and Platforms](https://docs.google.com/spreadsheets/u/2/d/10pPQYmyNnYb6zshOKxBjJ704E0XUj2vJ9HCDfoZxAoA/htmlview#)\n* [Awesome AI Guidelines](https://github.com/EthicalML/awesome-artificial-intelligence-guidelines) | ![](https://img.shields.io/github/stars/EthicalML/awesome-artificial-intelligence-guidelines?style=social)\n* [Awesome interpretable machine learning](https://github.com/lopusz/awesome-interpretable-machine-learning) | ![](https://img.shields.io/github/stars/lopusz/awesome-interpretable-machine-learning?style=social)\n* [Awesome MLOps](https://github.com/visenger/awesome-mlops) | ![](https://img.shields.io/github/stars/visenger/awesome-mlops?style=social)\n* [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-machine-learning-operations) | ![](https://img.shields.io/github/stars/EthicalML/awesome-machine-learning-operations?style=social)\n* [Awesome Responsible AI](https://github.com/AthenaCore/AwesomeResponsibleAI) | ![](https://img.shields.io/github/stars/AthenaCore/AwesomeResponsibleAI?style=social) | AthenaCore\n* [Awesome-explainable-AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI/) | ![](https://img.shields.io/github/stars/wangyongjie-ntu/Awesome-explainable-AI?style=social)\n* [Awesome-ML-Model-Governance](https://github.com/visenger/Awesome-ML-Model-Governance) | ![](https://img.shields.io/github/stars/visenger/Awesome-ML-Model-Governance?style=social)\n* [awesomelistsio/Awesome AI Ethics](https://github.com/awesomelistsio/awesome-ai-ethics) | ![](https://img.shields.io/github/stars/awesomelistsio/awesome-ai-ethics?style=social)\n* [Awful AI](https://github.com/daviddao/awful-ai) | ![](https://img.shields.io/github/stars/daviddao/awful-ai?style=social)\n* [Comments Received for RFI on Artificial Intelligence Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework/comments-received-rfi-artificial-intelligence-risk-management) | IMDA-BTG\n* [criticalML](https://github.com/rockita/criticalML) | ![](https://img.shields.io/github/stars/rockita/criticalML?style=social)\n* [Ethics for people who work in tech](https://ethicsforpeoplewhoworkintech.com/)\n* [Evaluation Repository for 'Sociotechnical Safety Evaluation of Generative AI Systems'](https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vQObeTxvXtOs--zd98qG2xBHHuTTJOyNISBJPthZFr3at2LCrs3rcv73d4of1A78JV2eLuxECFXJY43/pubhtml)\n* [Inventory of U.S. Department of Education AI Use Cases](https://www.ed.gov/about/ed-overview/artificial-intelligence-ai-guidance)\n* [GET Program for AI Ethics and Governance Standards](https://ieeexplore.ieee.org/browse/standards/get-program/page/series?id=93) | IEEE GET Program\n* [Global Digital Policy Roundup March 2025](https://www.techpolicy.press/global-digital-policy-roundup-march-2025/)\n* [LLM-Evals-Catalogue](https://github.com/IMDA-BTG/LLM-Evals-Catalogue) | ![](https://img.shields.io/github/stars/IMDA-BTG/LLM-Evals-Catalogue?style=social) | IMDA-BTG\n* [Machine Learning Ethics References](https://github.com/radames/Machine-Learning-Ethics-References) | ![](https://img.shields.io/github/stars/radames/Machine-Learning-Ethics-References?style=social)\n* [Machine Learning Interpretability Resources](https://github.com/h2oai/mli-resources) | ![](https://img.shields.io/github/stars/h2oai/mli-resources?style=social)\n* [MIT AI Agent Index](https://aiagentindex.mit.edu/)\n* [OECD-NIST Catalogue of AI Tools and Metrics](https://oecd.ai/en/catalogue/overview)\n* [OpenAI Cookbook](https://github.com/openai/openai-cookbook/tree/main) | ![](https://img.shields.io/github/stars/openai/openai-cookbook?style=social)\n* [private-ai-resources](https://github.com/OpenMined/private-ai-resources) | ![](https://img.shields.io/github/stars/OpenMined/private-ai-resources?style=social)\n* [Ravit Dotan's Resources](https://www.techbetter.ai/resources)\n* [ResponsibleAI](https://romanlutz.github.io/ResponsibleAI/)\n* [Tech & Ethics Curricula](https://docs.google.com/spreadsheets/d/1Z0DqQeZ-Aeq6LmD17J5m8zeeIR6ywWnH-WO-jWtXE9M/edit#gid=0)\n* [Ultraopxt/Awesome AI Ethics & Safety](https://github.com/Ultraopxt/Awesome-AI-Ethics-Safety) | ![](https://img.shields.io/github/stars/Ultraopxt/Awesome-AI-Ethics-Safety/?style=social)\n* [XAI Resources](https://github.com/pbiecek/xai_resources) | ![](https://img.shields.io/github/stars/pbiecek/xai_resources?style=social)\n* [xaience](https://github.com/andreysharapov/xaience) | ![](https://img.shields.io/github/stars/andreysharapov/xaience?style=social)\n\n## Technical Resources\n\n### Benchmarks\n\nThis section contains benchmarks or datasets used for benchmarks for ML systems, particularly those related to responsible ML desiderata.\n\n| Resource | Description |\n| --- | --- |\n| [benchm-ml](https://github.com/szilard/benchm-ml)-![](https://img.shields.io/github/stars/szilard/benchm-ml?style=social) | \"A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).\" |\n| [Bias Benchmark for QA dataset-BBQ](https://github.com/nyu-mll/bbq)-![](https://img.shields.io/github/stars/nyu-mll/bbq?style=social) | \"Repository for the Bias Benchmark for QA dataset.\" |\n| [Cataloguing LLM Evaluations](https://github.com/IMDA-BTG/LLM-Evals-Catalogue)-![](https://img.shields.io/github/stars/IMDA-BTG/LLM-Evals-Catalogue?style=social) | \"This repository stems from our paper, 'Cataloguing LLM Evaluations,' and serves as a living, collaborative catalogue of LLM evaluation frameworks, benchmarks and papers.\" |\n| [DecodingTrust](https://github.com/AI-secure/DecodingTrust)-![](https://img.shields.io/github/stars/huggingface/evaluate?style=social) | \"A Comprehensive Assessment of Trustworthiness in GPT Models.\" |\n| [EleutherAI, Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness)-![](https://img.shields.io/github/stars/EleutherAI/lm-evaluation-harness?style=social) | \"A framework for few-shot evaluation of language models.\" |\n| [Evidently AI 100+ LLM benchmarks and evaluation datasets](https://www.evidentlyai.com/llm-evaluation-benchmarks-datasets) | \"A database of LLM benchmarks and datasets to evaluate the performance of language models.\" |\n| [GEM](https://gem-benchmark.com/) | \"GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics.\" |\n| [HELM](https://crfm.stanford.edu/helm/latest/) | \"A holistic framework for evaluating foundation models.\" |\n| [Hugging Face, evaluate](https://github.com/huggingface/evaluate)-![](https://img.shields.io/github/stars/huggingface/evaluate?style=social) | \"Evaluate: A library for easily evaluating machine learning models and datasets.\" |\n| [i-gallegos, Fair-LLM-Benchmark](https://github.com/i-gallegos/Fair-LLM-Benchmark)-![](https://img.shields.io/github/stars/i-gallegos/Fair-LLM-Benchmark?style=social) | Benchmark from \"Bias and Fairness in Large Language Models: A Survey\" |\n| [jphall663, Generative AI Risk Management Resources](https://github.com/jphall663/gai_risk_management)-![](https://img.shields.io/github/stars/jphall663/gai_risk_management?style=social) | \"A place for ideas and drafts related to GAI risk management.\" |\n| [MLCommons, AI Luminate: A collaborative, transparent approach to safer AI](https://mlcommons.org/ailuminate/) | \"The AILuminate v1.1 benchmark suite is the first AI risk assessment benchmark developed with broad involvement from leading AI companies, academia, and civil society.\" |\n| [MLCommons, Introducing v0.5 of the AI Safety Benchmark from MLCommons](https://arxiv.org/pdf/2404.12241.pdf) | A paper about the MLCommons AI Safety Benchmark v0.5. |\n| [MLCommons, MLCommons AI Safety v0.5 Proof of Concept](https://mlcommons.org/2024/04/mlc-aisafety-v0-5-poc/) | \"The MLCommons AI Safety Benchmark aims to assess the safety of AI systems in order to guide development, inform purchasers and consumers, and support standards bodies and policymakers.\" |\n| [ML.ENERGY Leaderboard](https://ml.energy/leaderboard/?__theme=light) | \"Large language models (LLMs), especially the instruction-tuned ones, can generate human-like responses to chat prompts. Using Zeus for energy measurement, we created a leaderboard for LLM chat energy consumption.\" |\n| [ModelSlant.com](https://modelslant.com/#tab=topics) | \"How politically slanted are Large Language Models?\" |\n| [Nvidia MLPerf](https://www.nvidia.com/en-us/data-center/resources/mlperf-benchmarks/) | \"MLPerf™ benchmarks—developed by MLCommons, a consortium of AI leaders from academia, research labs, and industry—are designed to provide unbiased evaluations of training and inference performance for hardware, software, and services.\" |\n| [OpenML Benchmarking Suites](https://www.openml.org/search?type=benchmark&study_type=task) | OpenML's collection of over two dozen benchmarking suites. |\n| [Real Toxicity Prompts - Allen Institute for AI](https://allenai.org/data/real-toxicity-prompts) | \"A dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.\" |\n| [SafetyPrompts.com](https://safetyprompts.com/) | \"A Living Catalogue of Open Datasets for LLM Safety.\" |\n| [Sociotechnical Safety Evaluation Repository](https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vQObeTxvXtOs--zd98qG2xBHHuTTJOyNISBJPthZFr3at2LCrs3rcv73d4of1A78JV2eLuxECFXJY43/pubhtml) | An extensive spreadsheet of sociotechnical safety evaluations in a spreadsheet. |\n| [Trust-LLM-Benchmark Leaderboard](https://trustllmbenchmark.github.io/TrustLLM-Website/leaderboard.html) | A series of sortable leaderboards of LLMs based on different trustworthiness criteria. |\n| [TrustLLM-Benchmark](https://trustllmbenchmark.github.io/TrustLLM-Website/index.html) | \"A Comprehensive Study of Trustworthiness in Large Language Models.\" |\n| [TruthfulQA](https://github.com/sylinrl/TruthfulQA)-![](https://img.shields.io/github/stars/sylinrl/TruthfulQA?style=social) | \"TruthfulQA: Measuring How Models Imitate Human Falsehoods.\" |\n| [WAVES: Benchmarking the Robustness of Image Watermarks](https://wavesbench.github.io/) | \"This paper investigates the weaknesses of image watermarking techniques.\" |\n| [Wild-Time: A Benchmark of in-the-Wild Distribution Shifts over Time](https://github.com/huaxiuyao/Wild-Time)-![](https://img.shields.io/github/stars/huaxiuyao/Wild-Time?style=social) | \"Benchmark for Natural Temporal Distribution Shift (NeurIPS 2022).\" |\n| [Winogender Schemas](https://github.com/rudinger/winogender-schemas)-![](https://img.shields.io/github/stars/rudinger/winogender-schemas?style=social) | \"Data for evaluating gender bias in coreference resolution systems.\" |\n| [yandex-research - tabred](https://github.com/yandex-research/tabred)-![](https://img.shields.io/github/stars/yandex-research/tabred?style=social) | \"A Benchmark of Tabular Machine Learning in-the-Wild with real-world industry-grade tabular datasets.\" |\n\n### Common or Useful Datasets\n\nThis section contains datasets that are commonly used in responsible ML evaulations or repositories of interesting/important data sources.\n\n* [A dataset on EU legislation for the digital world](https://www.bruegel.org/dataset/dataset-eu-legislation-digital-world) | Bruegel\n* [Adult income dataset](https://www.kaggle.com/datasets/wenruliu/adult-income-dataset)\n* [Balanced Faces in the Wild](https://github.com/visionjo/facerec-bias-bfw) | ![](https://img.shields.io/github/stars/visionjo/facerec-bias-bfw?style=social)\n* [COMPAS Recidivism Risk Score Data and Analysis](https://www.propublica.org/datastore/dataset/compas-recidivism-risk-score-data-and-analysis)\n  * **Data Repositories**\n    * [All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club)\n    * [Amazon Open Data](https://registry.opendata.aws/amazon-reviews/)\n    * [Data.gov](https://data.gov/)\n    * [Home Mortgage Disclosure Act Data](https://www.consumerfinance.gov/data-research/hmda/)\n    * [MIMIC-III Clinical Database](https://physionet.org/content/mimiciii/1.4/)\n    * [UCI ML Data Repository](https://archive.ics.uci.edu/)\n* [FANNIE MAE Single Family Loan Performance](https://capitalmarkets.fanniemae.com/credit-risk-transfer/single-family-credit-risk-transfer/fannie-mae-single-family-loan-performance-data)\n* [German Credit Data](https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) | Statlog\n* [Have I Been Trained?](https://haveibeentrained.com/)\n* [nikhgarg / EmbeddingDynamicStereotypes](https://github.com/nikhgarg/EmbeddingDynamicStereotypes) | ![](https://img.shields.io/github/stars/nikhgarg/EmbeddingDynamicStereotypes?style=social)\n* [NYPD Stop, Question and Frisk Data](https://www.nyc.gov/site/nypd/stats/reports-analysis/stopfrisk.page)\n* [Presidential Deepfakes Dataset](https://www.media.mit.edu/publications/presidential-deepfakes-dataset/)\n* [socialfoundations / folktables](https://github.com/socialfoundations/folktables) | ![](https://img.shields.io/github/stars/socialfoundations/folktables?style=social)\n* [Wikipedia Talk Labels: Personal Attacks](https://www.kaggle.com/datasets/jigsaw-team/wikipedia-talk-labels-personal-attacks)\n\n### Domain-specific Software\n\nThis section curates specialized software tools aimed at responsible ML within specific domains, such as in healthcare, finance, or social sciences.\n\n### Machine Learning Environment Management Tools\n\nThis section contains open source or open access ML environment management software.\n\n| Resource | Description |\n|----------|-------|\n| [dvc](https://dvc.org/) | \"Manage and version images, audio, video, and text files in storage and organize your ML modeling process into a reproducible workflow.\" |\n| [gigantum](https://github.com/gigantum)-![gigantum stars](https://img.shields.io/github/stars/gigantum?style=social) | \"Building a better way to create, collaborate, and share data-driven science.\" |\n| [mlflow](https://mlflow.org/) | \"An open source platform for the machine learning lifecycle.\" |\n| [mlmd](https://github.com/google/ml-metadata)-![mlmd stars](https://img.shields.io/github/stars/google/ml-metadata?style=social) | \"For recording and retrieving metadata associated with ML developer and data scientist workflows.\" |\n| [modeldb](https://github.com/VertaAI/modeldb)-![modeldb stars](https://img.shields.io/github/stars/VertaAI/modeldb?style=social) | \"Open Source ML Model Versioning, Metadata, and Experiment Management.\" |\n| [neptune](https://neptune.ai/researchers) | \"A single place to manage all your model metadata.\" |\n| [Opik](https://github.com/comet-ml/opik)-![](https://img.shields.io/github/stars/comet-ml/opik?style=social) |  \"Evaluate, test, and ship LLM applications across your dev and production lifecycles.\" |\n\n### Personal Data Protection Tools\n\nThis section contains tools for personal data protection.\n\n| Name | Description |\n|------|-------------|\n| [LLM Dataset Inference: Did you train on my dataset?](https://github.com/pratyushmaini/llm_dataset_inference/)-![](https://img.shields.io/github/stars/pratyushmaini/llm_dataset_inference?style=social) | \"Official Repository for Dataset Inference for LLMs\" |\n\n### Open Source/Access Responsible AI Software Packages\n\nThis section contains open source or open access software used to implement responsible ML. As much as possible, descriptions are quoted verbatim from the respective repositories themselves. In rare instances, we provide our own descriptions (unmarked by quotes).\n\n#### Browser\n\n| Name | Description |\n|------|-------------|\n| [DiscriLens](https://github.com/wangqianwen0418/DiscriLens)-![](https://img.shields.io/github/stars/wangqianwen0418/DiscriLens?style=social) | \"Discrimination in Machine Learning.\" |\n| [Hugging Face, BiasAware: Dataset Bias Detection](https://huggingface.co/spaces/avid-ml/biasaware) | \"BiasAware is a specialized tool for detecting and quantifying biases within datasets used for Natural Language Processing (NLP) tasks.\" |\n| [manifold](https://github.com/uber/manifold)-![](https://img.shields.io/github/stars/uber/manifold?style=social) | \"A model-agnostic visual debugging tool for machine learning.\" |\n| [PAIR-code - datacardsplaybook](https://github.com/PAIR-code/datacardsplaybook)-![](https://img.shields.io/github/stars/PAIR-code/datacardsplaybook?style=social) | \"The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation.\" |\n| [PAIR-code - facets](https://github.com/PAIR-code/facets)-![](https://img.shields.io/github/stars/PAIR-code/facets?style=social) | \"Visualizations for machine learning datasets.\" |\n| [PAIR-code - knowyourdata](https://github.com/pair-code/knowyourdata)-![](https://img.shields.io/github/stars/PAIR-code/knowyourdata?style=social) | \"A tool to help researchers and product teams understand datasets with the goal of improving data quality, and mitigating fairness and bias issues.\" |\n| [TensorBoard Projector](http://projector.tensorflow.org) | \"Using the TensorBoard Embedding Projector, you can graphically represent high dimensional embeddings. This can be helpful in visualizing, examining, and understanding your embedding layers.\" |\n| [What-if Tool](https://pair-code.github.io/what-if-tool/index.html#about) | \"Visually probe the behavior of trained machine learning models, with minimal coding.\" |\n\n#### C/C++\n\n| Name | Description |\n|------|-------------|\n| [Born-again Tree Ensembles](https://github.com/vidalt/BA-Trees)-![](https://img.shields.io/github/stars/vidalt/BA-Trees?style=social) | \"Born-Again Tree Ensembles: Transforms a random forest into a single, minimal-size, tree with exactly the same prediction function in the entire feature space (ICML 2020).\" |)\n| [Certifiably Optimal RulE ListS](https://github.com/nlarusstone/corels)-![](https://img.shields.io/github/stars/nlarusstone/corels?style=social) | \"CORELS is a custom discrete optimization technique for building rule lists over a categorical feature space.\" |\n| [Secure-ML](https://github.com/shreya-28/Secure-ML)-![](https://img.shields.io/github/stars/shreya-28/Secure-ML?style=social) | \"Secure Linear Regression in the Semi-Honest Two-Party Setting.\" |\n\n#### JavaScript\n\n| Name | Description |\n|------|-------------|\n| [LDNOOBW](https://github.com/LDNOOBW)-![](https://img.shields.io/github/stars/LDNOOBW?style=social) | \"List of Dirty, Naughty, Obscene, and Otherwise Bad Words\" |\n\n#### Python\n\n| Name | Description |\n|------|-------------|\n| [acd](https://github.com/csinva/hierarchical_dnn_interpretations)-![](https://img.shields.io/github/stars/csinva/hierarchical_dnn_interpretations?style=social) | \"Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code for *Hierarchical interpretations for neural network predictions*.” |\n| [aequitas](https://github.com/dssg/aequitas)-![](https://img.shields.io/github/stars/dssg/aequitas?style=social) | \"Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive tools.” |\n| [AI Explainability 360](https://github.com/IBM/AIX360)-![](https://img.shields.io/github/stars/IBM/AIX360?style=social) | \"Interpretability and explainability of data and machine learning models.” |\n| [AI Fairness 360](https://github.com/Trusted-AI/AIF360)-![](https://img.shields.io/github/stars/Trusted-AI/AIF360?style=social) | \"A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.” |\n| [ALEPython](https://github.com/blent-ai/ALEPython)-![](https://img.shields.io/github/stars/blent-ai/ALEPython?style=social) | \"Python Accumulated Local Effects package.” |\n| [Aletheia](https://github.com/SelfExplainML/Aletheia)-![](https://img.shields.io/github/stars/SelfExplainML/Aletheia?style=social) | \"A Python package for unwrapping ReLU DNNs.” |\n| [algofairness](https://github.com/algofairness)-![](https://img.shields.io/github/stars/algofairness?style=social) | See [Algorithmic Fairness](http://fairness.haverford.edu/). |\n| [Alibi](https://github.com/SeldonIO/alibi)-![](https://img.shields.io/github/stars/SeldonIO/alibi?style=social) | \"Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.” |\n| [allennlp](https://github.com/allenai/allennlp)-![](https://img.shields.io/github/stars/allenai/allennlp?style=social) | \"An open-source NLP research library, built on PyTorch.” |\n| [anchor](https://github.com/marcotcr/anchor)-![](https://img.shields.io/github/stars/marcotcr/anchor?style=social) | \"Code for 'High-Precision Model-Agnostic Explanations' paper.” |\n| [Bayesian Case Model](https://users.cs.duke.edu/~cynthia/code/BCM.zip)\n| [Bayesian Ors-Of-Ands](https://github.com/wangtongada/BOA)-![](https://img.shields.io/github/stars/wangtongada/BOA?style=social) | \"This code implements the Bayesian or-of-and algorithm as described in the BOA paper. We include the tictactoe dataset in the correct formatting to be used by this code.” |\n| [Bayesian Rule List - BRL](https://users.cs.duke.edu/~cynthia/code/BRL_supplement_code.zip) | Rudin group at Duke Bayesian case model implementation |\n| [BlackBoxAuditing](https://github.com/algofairness/BlackBoxAuditing)-![](https://img.shields.io/github/stars/algofairness/BlackBoxAuditing?style=social) | \"Research code for auditing and exploring black box machine-learning models.” |\n| [CalculatedContent, WeightWatcher](https://github.com/calculatedcontent/weightwatcher)-![](https://img.shields.io/github/stars/calculatedcontent/weightwatcher?style=social) | \"The WeightWatcher tool for predicting the accuracy of Deep Neural Networks.\" |\n| [captum](https://github.com/pytorch/captum)-![](https://img.shields.io/github/stars/pytorch/captum?style=social) | \"Model interpretability and understanding for PyTorch.” |\n| [casme](https://github.com/kondiz/casme)-![](https://img.shields.io/github/stars/kondiz/casme?style=social) | \"contains the code originally forked from the ImageNet training in PyTorch that is modified to present the performance of classifier-agnostic saliency map extraction, a practical algorithm to train a classifier-agnostic saliency mapping by simultaneously training a classifier and a saliency mapping.” |\n| [Causal Discovery Toolbox](https://github.com/FenTechSolutions/CausalDiscoveryToolbox)-![](https://img.shields.io/github/stars/FenTechSolutions/CausalDiscoveryToolbox?style=social) | \"Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.” |\n| [causalml](https://github.com/uber/causalml)-![](https://img.shields.io/github/stars/uber/causalml?style=social) | \"Uplift modeling and causal inference with machine learning algorithms.” |\n| [cdt15, Causal Discovery Lab., Shiga University](https://github.com/cdt15)-![](https://img.shields.io/github/stars/cdt15?style=social) | \"LiNGAM is a new method for estimating structural equation models or linear causal Bayesian networks. It is based on using the non-Gaussianity of the data.\" |\n| [checklist](https://github.com/marcotcr/checklist)-![](https://img.shields.io/github/stars/marcotcr/checklist?style=social) | \"Beyond Accuracy: Behavioral Testing of NLP models with CheckList.” |\n| [cleverhans](https://github.com/cleverhans-lab/cleverhans)-![](https://img.shields.io/github/stars/cleverhans-lab/cleverhans?style=social) | \"An adversarial example library for constructing attacks, building defenses, and benchmarking both.” |\n| [contextual-AI](https://github.com/SAP/contextual-ai)-![](https://img.shields.io/github/stars/SAP/contextual-ai?style=social) | \"Contextual AI adds explainability to different stages of machine learning pipelines | data, training, and inference | thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.” |\n| [ContrastiveExplanation - Foil Trees](https://github.com/MarcelRobeer/ContrastiveExplanation)-![](https://img.shields.io/github/stars/MarcelRobeer/ContrastiveExplanation?style=social) | \"provides an explanation for why an instance had the current outcome (fact) rather than a targeted outcome of interest (foil). These counterfactual explanations limit the explanation to the features relevant in distinguishing fact from foil, thereby disregarding irrelevant features.” |\n| [counterfit](https://github.com/Azure/counterfit/)-![](https://img.shields.io/github/stars/Azure/counterfit?style=social) | \"a CLI that provides a generic automation layer for assessing the security of ML models.” |\n| [dalex](https://github.com/ModelOriented/DALEX)-![](https://img.shields.io/github/stars/ModelOriented/DALEX?style=social) | \"moDel Agnostic Language for Exploration and eXplanation.” |\n| [debiaswe](https://github.com/tolga-b/debiaswe)-![](https://img.shields.io/github/stars/tolga-b/debiaswe?style=social) | \"Remove problematic gender bias from word embeddings.” |\n| [DeepExplain](https://github.com/marcoancona/DeepExplain)-![](https://img.shields.io/github/stars/marcoancona/DeepExplain?style=social) | \"provides a unified framework for state-of-the-art gradient and perturbation-based attribution methods. It can be used by researchers and practitioners for better undertanding the recommended existing models, as well for benchmarking other attribution methods.” |\n| [DeepLIFT](https://github.com/kundajelab/deeplift)-![](https://img.shields.io/github/stars/kundajelab/deeplift?style=social) | \"This repository implements the methods in 'Learning Important Features Through Propagating Activation Differences' by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, gradient-times-input (equivalent to a version of Layerwise Relevance Propagation for ReLU networks), guided backprop and integrated gradients.” |\n| [deepvis](https://github.com/yosinski/deep-visualization-toolbox)-![](https://img.shields.io/github/stars/yosinski/deep-visualization-toolbox?style=social) | \"the code required to run the Deep Visualization Toolbox, as well as to generate the neuron-by-neuron visualizations using regularized optimization.” |\n| [DIANNA](https://github.com/dianna-ai/dianna)-![](https://img.shields.io/github/stars/dianna-ai/dianna?style=social) | \"DIANNA is a Python package that brings explainable AI (XAI) to your research project. It wraps carefully selected XAI methods in a simple, uniform interface. It's built by, with and for (academic) researchers and research software engineers working on machine learning projects.” |\n| [DiCE](https://github.com/interpretml/DiCE)-![](https://img.shields.io/github/stars/interpretml/DiCE?style=social) | \"Generate Diverse Counterfactual Explanations for any machine learning model.” |\n| [DoWhy](https://github.com/microsoft/dowhy)-![](https://img.shields.io/github/stars/microsoft/dowhy?style=social) | \"DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.” |\n| [dtreeviz](https://github.com/parrt/dtreeviz)-![](https://img.shields.io/github/stars/parrt/dtreeviz?style=social) | \"A python library for decision tree visualization and model interpretation.” |\n| [ecco](https://github.com/jalammar/ecco)-![](https://img.shields.io/github/stars/jalammar/ecco?style=social) | \"Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).” |\n| [effector](https://github.com/givasile/effector)-![](https://img.shields.io/github/stars/givasile/effector?style=social) | \"eXplainable AI for Tabular Data\" |\n| [eli5](https://github.com/TeamHG-Memex/eli5)-![](https://img.shields.io/github/stars/TeamHG-Memex/eli5?style=social) | \"A library for debugging/inspecting machine learning classifiers and explaining their predictions.” |\n| [explabox](https://github.com/MarcelRobeer/explabox)-![](https://img.shields.io/github/stars/MarcelRobeer/explabox?style=social) | \"aims to support data scientists and machine learning (ML) engineers in explaining, testing and documenting AI/ML models, developed in-house or acquired externally. The explabox turns your ingestibles (AI/ML model and/or dataset) into digestibles (statistics, explanations or sensitivity insights).” |\n| [Explainable Boosting Machine EBM/GA2M](https://github.com/interpretml/interpret)-![](https://img.shields.io/github/stars/interpretml/interpret?style=social) | \"an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.” |\n| [ExplainaBoard](https://github.com/neulab/ExplainaBoard)-![](https://img.shields.io/github/stars/neulab/ExplainaBoard?style=social) | \"a tool that inspects your system outputs, identifies what is working and what is not working, and helps inspire you with ideas of where to go next.” |\n| [explainerdashboard](https://github.com/oegedijk/explainerdashboard)-![](https://img.shields.io/github/stars/oegedijk/explainerdashboard?style=social) | \"Quickly build Explainable AI dashboards that show the inner workings of so-called \"blackbox\" machine learning models.” |\n| [explainX](https://github.com/explainX/explainx)-![](https://img.shields.io/github/stars/explainX/explainx?style=social) | \"Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.” |\n| [fair-classification](https://github.com/mbilalzafar/fair-classification)-![](https://img.shields.io/github/stars/mbilalzafar/fair-classification?style=social) | \"Python code for training fair logistic regression classifiers.” |\n| [fairlearn](https://github.com/fairlearn/fairlearn)-![](https://img.shields.io/github/stars/fairlearn/fairlearn?style=social) | \"a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage.” |\n| [fairml](https://github.com/adebayoj/fairml)-![](https://img.shields.io/github/stars/adebayoj/fairml?style=social) | \"a python toolbox auditing the machine learning models for bias.” |\n| [fairness_measures_code](https://github.com/megantosh/fairness_measures_code)-![](https://img.shields.io/github/stars/megantosh/fairness_measures_code?style=social) | \"contains implementations of measures used to quantify discrimination.” |\n| [fairness-comparison](https://github.com/algofairness/fairness-comparison)-![](https://img.shields.io/github/stars/algofairness/fairness-comparison?style=social) | \"meant to facilitate the benchmarking of fairness aware machine learning algorithms.” |\n| [Falling Rule List - FRL](https://users.cs.duke.edu/~cynthia/code/falling_rule_list.zip) | Rudin group at Duke falling rule list implementation |\n| [foolbox](https://github.com/bethgelab/foolbox)-![](https://img.shields.io/github/stars/bethgelab/foolbox?style=social) | \"A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX.” |\n| [Giskard](https://github.com/Giskard-AI/giskard)-![](https://img.shields.io/github/stars/Giskard-AI/giskard?style=social) | \"The testing framework dedicated to ML models, from tabular to LLMs. Scan AI models to detect risks of biases, performance issues and errors. In 4 lines of code.” |\n| [gplearn](https://github.com/trevorstephens/gplearn)-![](https://img.shields.io/github/stars/trevorstephens/gplearn?style=social) | \"implements Genetic Programming in Python, with a scikit-learn inspired and compatible API.” |\n| [Grad-CAM](https://github.com/topics/grad-cam)-(GitHub topic) | Grad-CAM is a technique for making convolutional neural networks more transparent by visualizing the regions of input that are important for predictions in computer vision models. |\n| [H2O-3 Monotonic GBM](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogradientboostingestimator) | \"Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data set.\" |\n| [H2O-3 Penalized Generalized Linear Models](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlinearestimator) | \"Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.\" |\n| [H2O-3 Sparse Principal Components](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlowrankestimator) | \"Builds a generalized low rank decomposition of an H2O data frame.\" |\n| [h2o-LLM-eval](https://github.com/h2oai/h2o-LLM-eval)-![](https://img.shields.io/github/stars/h2oai/h2o-LLM-eval?style=social) | \"Large-language Model Evaluation framework with Elo Leaderboard and A-B testing.\" |\n| [hate-functional-tests](https://github.com/paul-rottger/hate-functional-tests)-![](https://img.shields.io/github/stars/paul-rottger/hate-functional-tests?style=social) | HateCheck: A dataset and test suite from an ACL 2021 paper, offering functional tests for hate speech detection models, including extensive case annotations and testing functionalities. |\n| [imodels](https://github.com/csinva/imodels)-![](https://img.shields.io/github/stars/csinva/imodels?style=social) | \"Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.” |\n| [iNNvestigate neural nets](https://github.com/albermax/innvestigate)-![](https://img.shields.io/github/stars/albermax/innvestigate?style=social) | A comprehensive Python library to analyze and interpret neural network behaviors in Keras, featuring a variety of methods like Gradient, LRP, and Deep Taylor. |\n| [Integrated-Gradients](https://github.com/ankurtaly/Integrated-Gradients)-![](https://img.shields.io/github/stars/ankurtaly/Integrated-Gradients?style=social) | \"a variation on computing the gradient of the prediction output w.r.t. features of the input. It requires no modification to the original network, is simple to implement, and is applicable to a variety of deep models (sparse and dense, text and vision).” |\n| [interpret_with_rules](https://github.com/clips/interpret_with_rules)-![](https://img.shields.io/github/stars/clips/interpret_with_rules?style=social) | \"induces rules to explain the predictions of a trained neural network, and optionally also to explain the patterns that the model captures from the training data, and the patterns that are present in the original dataset.” |\n| [interpret](https://github.com/interpretml/interpret)-![](https://img.shields.io/github/stars/interpretml/interpret?style=social) | \"an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof.” |\n| [InterpretME](https://github.com/SDM-TIB/InterpretME)-![](https://img.shields.io/github/stars/SDM-TIB/InterpretME?style=social) | \"integrates knowledge graphs (KG) with machine learning methods to generate interesting meaningful insights. It helps to generate human- and machine-readable decisions to provide assistance to users and enhance efficiency.” |\n| [keract](https://github.com/philipperemy/keract/)-![](https://img.shields.io/github/stars/philipperemy/keract?style=social) | Keract is a tool for visualizing activations and gradients in Keras models; it's meant to support a wide range of Tensorflow versions and to offer an intuitive API with Python examples. |\n| [Keras-vis](https://github.com/raghakot/keras-vis)-![](https://img.shields.io/github/stars/raghakot/keras-vis?style=social) | \"a high-level toolkit for visualizing and debugging your trained keras neural net models.” |\n| [L2X](https://github.com/Jianbo-Lab/L2X)-![](https://img.shields.io/github/stars/Jianbo-Lab/L2X?style=social) | \"Code for replicating the experiments in the paper [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://arxiv.org/pdf/1802.07814.pdf) at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan.” |\n| [LangFair](https://github.com/cvs-health/langfair)-![](https://img.shields.io/github/stars/cvs-health/langfair?style=social) | \"LangFair is a Python library for conducting use-case level LLM bias and fairness assessments\"\n| [langtest](https://github.com/JohnSnowLabs/langtest)-![](https://img.shields.io/github/stars/JohnSnowLabs/langtest?style=social) | \"LangTest: Deliver Safe & Effective Language Models\" |\n| [learning-fair-representations](https://github.com/zjelveh/learning-fair-representations)-![](https://img.shields.io/github/stars/zjelveh/learning-fair-representations?style=social) | \"Python numba implementation of Zemel et al. 2013 <http://www.cs.toronto.edu/~toni/Papers/icml-final.pdf>\" |\n| [leeky: Leakage/contamination testing for black box language models](https://github.com/mjbommar/leeky)-![](https://img.shields.io/github/stars/mjbommar/leeky?style=social) | \"leeky - training data contamination techniques for blackbox models\" |\n| [leondz / garak, LLM vulnerability scanner](https://github.com/leondz/garak)-![](https://img.shields.io/github/stars/leondz/garak?style=social) | \"LLM vulnerability scanner\" |\n| [LiFT](https://github.com/linkedin/LiFT)-![](https://img.shields.io/github/stars/linkedin/LiFT?style=social) | \"The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness and the mitigation of bias in large-scale machine learning workflows. The measurement module includes measuring biases in training data, evaluating fairness metrics for ML models, and detecting statistically significant differences in their performance across different subgroups.” |\n| [lilac](https://github.com/lilacai/lilac)-![](https://img.shields.io/github/stars/lilacai/lilac?style=social) | \"Curate better data for LLMs.\" |\n| [lime](https://github.com/marcotcr/lime)-![](https://img.shields.io/github/stars/marcotcr/lime?style=social) | \"explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations).” |\n| [lit](https://github.com/pair-code/lit)-![](https://img.shields.io/github/stars/pair-code/lit?style=social) | \"The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks.” |\n| [LLM Dataset Inference: Did you train on my dataset?](https://github.com/pratyushmaini/llm_dataset_inference/)-![](https://img.shields.io/github/stars/pratyushmaini/llm_dataset_inference?style=social) | \"Official Repository for Dataset Inference for LLMs\" |\n| [lofo-importance](https://github.com/aerdem4/lofo-importance)-![](https://img.shields.io/github/stars/aerdem4/lofo-importance?style=social) | \"LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.” |\n| [lrp_toolbox](https://github.com/sebastian-lapuschkin/lrp_toolbox)-![](https://img.shields.io/github/stars/sebastian-lapuschkin/lrp_toolbox?style=social) | \"The Layer-wise Relevance Propagation (LRP) algorithm explains a classifer's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself.” |\n| [MindsDB](https://github.com/mindsdb/mindsdb)-![](https://img.shields.io/github/stars/mindsdb/mindsdb?style=social) | \"enables developers to build AI tools that need access to real-time data to perform their tasks.” |\n| [ml_privacy_meter](https://github.com/privacytrustlab/ml_privacy_meter)-![](https://img.shields.io/github/stars/privacytrustlab/ml_privacy_meter?style=social) | \"an open-source library to audit data privacy in statistical and machine learning algorithms. The tool can help in the data protection impact assessment process by providing a quantitative analysis of the fundamental privacy risks of a (machine learning) model.” |\n| [ml-fairness-gym](https://github.com/google/ml-fairness-gym)-![](https://img.shields.io/github/stars/google/ml-fairness-gym?style=social) | \"a set of components for building simple simulations that explore the potential long-run impacts of deploying machine learning-based decision systems in social environments.” |\n| [MLextend](http://rasbt.github.io/mlxtend/) | \"Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.” |\n| [mllp](https://github.com/12wang3/mllp)-![](https://img.shields.io/github/stars/12wang3/mllp?style=social) | \"This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: [Transparent Classification with Multilayer Logical Perceptrons and Random Binarization](https://arxiv.org/abs/1912.04695).” |\n| [Monotonic Constraints](http://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html) | Guide on implementing and understanding monotonic constraints in XGBoost models to enhance predictive performance with practical Python examples. |\n| [Multilayer Logical Perceptron - MLLP](https://github.com/12wang3/mllp)-![](https://img.shields.io/github/stars/12wang3/mllp?style=social) | \"This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: [Transparent Classification with Multilayer Logical Perceptrons and Random Binarization](https://arxiv.org/abs/1912.04695).” |\n| [OptBinning](https://github.com/guillermo-navas-palencia/optbinning)-![](https://img.shields.io/github/stars/guillermo-navas-palencia/optbinning?style=social) | \"a library written in Python implementing a rigorous and flexible mathematical programming formulation to solve the optimal binning problem for a binary, continuous and multiclass target type, incorporating constraints not previously addressed.” |\n| [Optimal Sparse Decision Trees](https://github.com/xiyanghu/OSDT)-![](https://img.shields.io/github/stars/xiyanghu/OSDT?style=social) | \"This accompanies the paper, [\"Optimal Sparse Decision Trees\"](https://arxiv.org/abs/1904.12847) by Xiyang Hu, Cynthia Rudin, and Margo Seltzer.” |\n| [parity-fairness](https://pypi.org/project/parity-fairness/) | \"This repository contains codes that demonstrate the use of fairness metrics, bias mitigations and explainability tool.” |\n| [PDPbox](https://github.com/SauceCat/PDPbox)-![](https://img.shields.io/github/stars/SauceCat/PDPbox?style=social) | \"Python Partial Dependence Plot toolbox. Visualize the influence of certain features on model predictions for supervised machine learning algorithms, utilizing partial dependence plots.” |\n| [PiML-Toolbox](https://github.com/SelfExplainML/PiML-Toolbox)-![](https://img.shields.io/github/stars/SelfExplainML/PiML-Toolbox?style=social) | \"a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports a growing list of inherently interpretable ML models.” |\n| [pjsaelin / Cubist](https://github.com/pjaselin/Cubist?tab=readme-ov-file)-![](https://img.shields.io/github/stars/pjaselin/Cubist?style=social) | \"A Python package for fitting Quinlan's Cubist regression model\" |\n| [Privacy-Preserving-ML](https://github.com/abhinav-bohra/Privacy-Preserving-ML)-![](https://img.shields.io/github/stars/abhinav-bohra/Privacy-Preserving-ML?style=social) | \"Implementation of privacy-preserving SVM assuming public model private data scenario (data in encrypted but model parameters are unencrypted) using adequate partial homomorphic encryption.” |\n| [ProtoPNet](https://github.com/cfchen-duke/)-![](https://img.shields.io/github/stars/cfchen-duke?style=social) | \"This code package implements the prototypical part network (ProtoPNet) from the paper \"This Looks Like That: Deep Learning for Interpretable Image Recognition\" (to appear at NeurIPS 2019), by Chaofan Chen (Duke University), Oscar Li| (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University).” |\n| [pyBreakDown](https://github.com/MI2DataLab/pyBreakDown)-![](https://img.shields.io/github/stars/MI2DataLab/pyBreakDown?style=social) | See [dalex](https://dalex.drwhy.ai/). |\n| [PyCEbox](https://github.com/AustinRochford/PyCEbox)-![](https://img.shields.io/github/stars/AustinRochford/PyCEbox?style=social) | \"Python Individual Conditional Expectation Plot Toolbox.” |\n| [pyGAM](https://github.com/dswah/pyGAM)-![](https://img.shields.io/github/stars/dswah/pyGAM?style=social) | \"Generalized Additive Models in Python.” |\n| [pymc3](https://github.com/pymc-devs/pymc3)-![](https://img.shields.io/github/stars/pymc-devs/pymc3?style=social) | \"PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.” |\n| [pySS3](https://github.com/sergioburdisso/pyss3)-![](https://img.shields.io/github/stars/sergioburdisso/pyss3?style=social) | \"The SS3 text classifier is a novel and simple supervised machine learning model for text classification which is interpretable, that is, it has the ability to naturally (self)explain its rationale.” |\n| [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam)-![](https://img.shields.io/github/stars/jacobgil/pytorch-grad-cam?style=social) | \"a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods.” |\n| [pytorch-innvestigate](https://github.com/fgxaos/pytorch-innvestigate)-![](https://img.shields.io/github/stars/fgxaos/pytorch-innvestigate?style=social) | \"PyTorch implementation of Keras already existing project: [https://github.com/albermax/innvestigate/](https://github.com/albermax/innvestigate/).” |\n| [Quantus](https://github.com/understandable-machine-intelligence-lab/Quantus)-![](https://img.shields.io/github/stars/understandable-machine-intelligence-lab/Quantus?style=social) | \"Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations.\" |\n| [rationale](https://github.com/taolei87/rcnn/tree/master/code/rationale)-![](https://img.shields.io/github/stars/taolei87/rcnn?style=social) | \"This directory contains the code and resources of the following paper: *\"Rationalizing Neural Predictions\". Tao Lei, Regina Barzilay and Tommi Jaakkola. EMNLP 2016. [PDF](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf) [Slides](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale_slides.pdf)*. The method learns to provide justifications, i.e. rationales, as supporting evidence of neural networks' prediction.” |\n| [responsibly](https://github.com/ResponsiblyAI/responsibly)-![](https://img.shields.io/github/stars/ResponsiblyAI/responsibly?style=social) | \"Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems.” |\n| [REVISE: REvealing VIsual biaSEs](https://github.com/princetonvisualai/revise-tool)-![](https://img.shields.io/github/stars/princetonvisualai/revise-tool?style=social) | \"A tool that automatically detects possible forms of bias in a visual dataset along the axes of object-based, attribute-based, and geography-based patterns, and from which next steps for mitigation are suggested.” |\n| [RISE](https://github.com/eclique/RISE)-![](https://img.shields.io/github/stars/eclique/RISE?style=social) | \"contains source code necessary to reproduce some of the main results in the paper: [Vitali Petsiuk](http://cs-people.bu.edu/vpetsiuk/), [Abir Das](http://cs-people.bu.edu/dasabir/), [Kate Saenko](http://ai.bu.edu/ksaenko.html) (BMVC, 2018) [and] [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://arxiv.org/abs/1806.07421).” |\n| [Risk-SLIM](https://github.com/ustunb/risk-SLIM)-![](https://img.shields.io/github/stars/ustunb/risk-SLIM?style=social) | \"a machine learning method to fit simple customized risk scores in python.” |\n| [robustness](https://github.com/MadryLab/robustness)-![](https://img.shields.io/github/stars/MadryLab/robustness?style=social) | \"a package we (students in the [MadryLab](http://madry-lab.ml/)) created to make training, evaluating, and exploring neural networks flexible and easy.” |\n| [SAGE](https://github.com/iancovert/sage/)-![](https://img.shields.io/github/stars/iancovert/sage?style=social) | \"SAGE (Shapley Additive Global importancE) is a game-theoretic approach for understanding black-box machine learning models. It quantifies each feature's importance based on how much predictive power it contributes, and it accounts for complex feature interactions using the Shapley value.” |\n| [SALib](https://github.com/SALib/SALib)-![](https://img.shields.io/github/stars/SALib/SALib?style=social) | \"Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.” |\n| [Scikit-Explain](https://scikit-explain.readthedocs.io/en/latest/index.html) | \"User-friendly Python module for machine learning explainability,\" featuring PD and ALE plots, LIME, SHAP, permutation importance and Friedman's H, among other methods. |\n| [scikit-fairness](https://github.com/koaning/scikit-fairness)-![](https://img.shields.io/github/stars/koaning/scikit-fairness?style=social) | Historical link. Merged with [fairlearn](https://fairlearn.org/). |\n| [Scikit-learn](https://scikit-learn.org/stable/) [Decision Trees](http://scikit-learn.org/stable/modules/tree.html) | \"a non-parametric supervised learning method used for classification and regression.” |\n| [Scikit-learn](https://scikit-learn.org/stable/) [Generalized Linear Models](http://scikit-learn.org/stable/modules/linear_model.html) | \"a set of methods intended for regression in which the target value is expected to be a linear combination of the features.” |\n| [Scikit-learn](https://scikit-learn.org/stable/) [Sparse Principal Components](http://scikit-learn.org/stable/modules/decomposition.html#sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca) | \"a variant of [principal component analysis, PCA], with the goal of extracting the set of sparse components that best reconstruct the data.” |\n| [scikit-multiflow](https://scikit-multiflow.github.io/) | \"a machine learning package for streaming data in Python.” |\n| [shap](https://github.com/slundberg/shap)-![](https://img.shields.io/github/stars/slundberg/shap?style=social) | \"a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions\"\n| [shapley](https://github.com/benedekrozemberczki/shapley)-![](https://img.shields.io/github/stars/benedekrozemberczki/shapley?style=social) | \"a Python library for evaluating binary classifiers in a machine learning ensemble.” |\n| [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys)-![](https://img.shields.io/github/stars/tmadl/sklearn-expertsys?style=social) | \"a scikit-learn compatible wrapper for the Bayesian Rule List classifier developed by Letham et al., 2015, extended by a minimum description length-based discretizer (Fayyad & Irani, 1993) for continuous data, and by an approach to subsample large datasets for better performance.” |\n| [skope-rules](https://github.com/scikit-learn-contrib/skope-rules)-![](https://img.shields.io/github/stars/scikit-learn-contrib/skope-rules?style=social) | \"a Python machine learning module built on top of scikit-learn and distributed under the 3-Clause BSD license.” |\n| [solas-ai-disparity](https://github.com/SolasAI/solas-ai-disparity)-![](https://img.shields.io/github/stars/SolasAI/solas-ai-disparity?style=social) | \"a collection of tools that allows modelers, compliance, and business stakeholders to test outcomes for bias or discrimination using widely accepted fairness metrics.” |\n| [Super-sparse Linear Integer models - SLIMs](https://github.com/ustunb/slim-python)-![](https://img.shields.io/github/stars/ustunb/slim-python?style=social) | \"a package to learn customized scoring systems for decision-making problems.” |\n| [tensorflow/fairness-indicators](https://github.com/tensorflow/fairness-indicators)-![](https://img.shields.io/github/stars/tensorflow/fairness-indicators?style=social) | \"designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader Tensorflow toolkit.” |\n| [tensorflow/lattice](https://github.com/tensorflow/lattice)-![](https://img.shields.io/github/stars/tensorflow/lattice?style=social) | \"a library that implements constrained and interpretable lattice based models. It is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow.” |\n| [tensorflow/lucid](https://github.com/tensorflow/lucid)-![](https://img.shields.io/github/stars/tensorflow/lucid?style=social) | \"a collection of infrastructure and tools for research in neural network interpretability.” |\n| [tensorflow/model-analysis](https://github.com/tensorflow/model-analysis)-![](https://img.shields.io/github/stars/tensorflow/model-analysis?style=social) | \"a library for evaluating TensorFlow models. It allows users to evaluate their models on large amounts of data in a distributed manner, using the same metrics defined in their trainer. These metrics can be computed over different slices of data and visualized in Jupyter notebooks.” |\n| [tensorflow/model-card-toolkit](https://github.com/tensorflow/model-card-toolkit)-![](https://img.shields.io/github/stars/tensorflow/model-card-toolkit?style=social) | \"streamlines and automates generation of Model Cards, machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables you to share model metadata and metrics with researchers, developers, reporters, and more.” |\n| [tensorflow/model-remediation](https://github.com/tensorflow/model-remediation)-![](https://img.shields.io/github/stars/tensorflow/model-remediation?style=social) | \"a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.” |\n| [tensorflow/privacy](https://github.com/tensorflow/privacy)-![](https://img.shields.io/github/stars/tensorflow/privacy?style=social) | \"the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided.” |\n| [tensorflow/tcav](https://github.com/tensorflow/tcav)-![](https://img.shields.io/github/stars/tensorflow/tcav?style=social) | \"Testing with Concept Activation Vectors (TCAV) is a new interpretability method to understand what signals your neural networks models uses for prediction.” |\n| [tensorfuzz](https://github.com/brain-research/tensorfuzz)-![](https://img.shields.io/github/stars/brain-research/tensorfuzz?style=social) | \"a library for performing coverage guided fuzzing of neural networks.” |\n| [TensorWatch](https://github.com/microsoft/tensorwatch)-![](https://img.shields.io/github/stars/microsoft/tensorwatch?style=social) | \"a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.” |\n| [text_explainability](https://text-explainability.readthedocs.io/) | \"text_explainability provides a generic architecture from which well-known state-of-the-art explainability approaches for text can be composed.” |\n| [text_sensitivity](https://text-sensitivity.readthedocs.io/) | \"Uses the generic architecture of text_explainability to also include tests of safety (how safe it the model in production, i.e. types of inputs it can handle), robustness (how generalizable the model is in production, e.g. stability when adding typos, or the effect of adding random unrelated data) and fairness (if equal individuals are treated equally by the model, e.g. subgroup fairness on sex and nationality).” |\n| [TextFooler](https://github.com/jind11/TextFooler)-![](https://img.shields.io/github/stars/jind11/TextFooler?style=social) | \"A Model for Natural Language Attack on Text Classification and Inference\"\n| [tf-explain](https://github.com/sicara/tf-explain)-![](https://img.shields.io/github/stars/sicara/tf-explain?style=social) | \"Implements interpretability methods as Tensorflow 2.x callbacks to ease neural network's understanding.” |\n| [themis-ml](https://github.com/cosmicBboy/themis-ml)-![](https://img.shields.io/github/stars/cosmicBboy/themis-ml?style=social) | \"A Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms.” |\n| [Themis](https://github.com/LASER-UMASS/Themis)-![](https://img.shields.io/github/stars/LASER-UMASS/Themis?style=social) | \"A testing-based approach for measuring discrimination in a software system.” |\n| [TorchUncertainty](https://github.com/ENSTA-U2IS/torch-uncertainty)-![](https://img.shields.io/github/stars/ENSTA-U2IS/torch-uncertainty?style=social) | \"A package designed to help you leverage uncertainty quantification techniques and make your deep neural networks more reliable.” |\n| [treeinterpreter](https://github.com/andosa/treeinterpreter)-![](https://img.shields.io/github/stars/andosa/treeinterpreter?style=social) | \"Package for interpreting scikit-learn's decision tree and random forest predictions.” |\n| [TRIAGE](https://github.com/seedatnabeel/TRIAGE)-![](https://img.shields.io/github/stars/seedatnabeel/TRIAGE?style=social) | \"This repository contains the implementation of TRIAGE, a \"Data-Centric AI\" framework for data characterization tailored for regression.” |\n| [woe](https://github.com/boredbird/woe)-![](https://img.shields.io/github/stars/boredbird/woe?style=social) | \"Tools for WoE Transformation mostly used in ScoreCard Model for credit rating.” |\n| [xai](https://github.com/EthicalML/xai)-![](https://img.shields.io/github/stars/EthicalML/xai?style=social) | \"A Machine Learning library that is designed with AI explainability in its core.” |\n| [xdeep](https://github.com/datamllab/xdeep)-![](https://img.shields.io/github/stars/datamllab/xdeep?style=social) | \"An open source Python library for Interpretable Machine Learning.” |\n| [XGBoost](http://xgboost.readthedocs.io/en/latest/) | \"an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.” |\n| [xplique](https://github.com/deel-ai/xplique)-![](https://img.shields.io/github/stars/deel-ai/xplique?style=social) | \"A Python toolkit dedicated to explainability. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models.” |\n| [ydata-profiling](https://github.com/ydataai/ydata-profiling)-![](https://img.shields.io/github/stars/ydataai/ydata-profiling?style=social) | \"Provide(s) a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution.” |\n| [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick)-![](https://img.shields.io/github/stars/DistrictDataLabs/yellowbrick?style=social) | \"A suite of visual diagnostic tools called \"Visualizers\" that extend the scikit-learn API to allow human steering of the model selection process.” |\n\n#### R\n\n| Name | Description |\n|------|-------------|\n| [ALEPlot](https://cran.r-project.org/web/packages/ALEPlot/index.html) | \"Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models.\"  |\n| [arules](https://cran.r-project.org/web/packages/arules/index.html) | \"Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005).\" |\n| [Causal SVM](https://github.com/shangtai/githubcausalsvm)-![](https://img.shields.io/github/stars/shangtai/githubcausalsvm?style=social) | \"We present a new machine learning approach to estimate whether a treatment has an effect on an individual, in the setting of the classical potential outcomes framework with binary outcomes.\" |\n| [DALEX](https://github.com/ModelOriented/DALEX)-![](https://img.shields.io/github/stars/ModelOriented/DALEX?style=social) | \"moDel Agnostic Language for Exploration and eXplanation.\" |\n| [DALEXtra: Extension for 'DALEX' Package](https://cran.r-project.org/web/packages/DALEXtra/index.html) | \"Provides wrapper of various machine learning models.\" |\n| [DrWhyAI](https://github.com/ModelOriented/DrWhy)-![](https://img.shields.io/github/stars/ModelOriented/DrWhy?style=social) | \"DrWhy is [a] collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.\" |\n| [elasticnet](https://cran.r-project.org/web/packages/elasticnet/index.html) | \"Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for doing sparse PCA.\" |\n| [Explainable Boosting Machine - EBM/GA2M](https://cran.r-project.org/web/packages/interpret/index.html) | \"Package for training interpretable machine learning models.\" |\n| [ExplainPrediction](https://github.com/rmarko/ExplainPrediction)-![](https://img.shields.io/github/stars/rmarko/ExplainPrediction?style=social) | \"Generates explanations for classification and regression models and visualizes them.\" |\n| [fairmodels](https://github.com/ModelOriented/fairmodels)-![](https://img.shields.io/github/stars/ModelOriented/fairmodels?style=social) | \"Flexible tool for bias detection, visualization, and mitigation. Use models explained with DALEX and calculate fairness classification metrics based on confusion matrices using fairness_check() or try newly developed module for regression models using fairness_check_regression().\" |\n| [fairness](https://cran.r-project.org/web/packages/fairness/index.html) | \"Offers calculation, visualization and comparison of algorithmic fairness metrics.\" |\n| [fastshap](https://github.com/bgreenwell/fastshap)-![](https://img.shields.io/github/stars/bgreenwell/fastshap?style=social) | \"The goal of fastshap is to provide an efficient and speedy approach (at least relative to other implementations) for computing approximate Shapley values, which help explain the predictions from any machine learning model.\" |\n| [featureImportance](https://github.com/giuseppec/featureImportance)-![](https://img.shields.io/github/stars/giuseppec/featureImportance?style=social) | \"An extension for the mlr package and allows to compute the permutation feature importance in a model-agnostic manner.\" |\n| [flashlight](https://github.com/mayer79/flashlight)-![](https://img.shields.io/github/stars/mayer79/flashlight?style=social) | \"The goal of this package is [to] shed light on black box machine learning models.\" |\n| [forestmodel](https://cran.r-project.org/web/packages/forestmodel/index.html) | \"Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph().\" |\n| [fscaret](https://cran.r-project.org/web/packages/fscaret/) | \"Automated feature selection using variety of models provided by 'caret' package.\" |\n| [gam](https://cran.r-project.org/web/packages/gam/index.html) | \"Functions for fitting and working with generalized additive models, as described in chapter 7 of \"Statistical Models in S\" (Chambers and Hastie (eds), 1991), and \"Generalized Additive Models\" (Hastie and Tibshirani, 1990).\" |\n| [glm2](https://cran.r-project.org/web/packages/glm2/) | \"Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.\" |\n| [glmnet](https://cran.r-project.org/web/packages/glmnet/index.html) | \"Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression.\" |\n| [H2O-3 Monotonic GBM](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.gbm.html) | \"Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data set.\" |\n| [H2O-3 Penalized Generalized Linear Models](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.glm.html) | \"Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.\" |\n| [H2O-3 Sparse Principal Components](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.glrm.html) | \"Builds a generalized low rank decomposition of an H2O data frame.\" |\n| [iBreakDown](https://github.com/ModelOriented/iBreakDown)-![](https://img.shields.io/github/stars/ModelOriented/iBreakDown?style=social) | \"A model agnostic tool for explanation of predictions from black boxes ML models.\"|\n| [ICEbox: Individual Conditional Expectation Plot Toolbox](https://cran.r-project.org/web/packages/ICEbox/index.html) | \"Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm.\"|\n| [iml](https://github.com/christophM/iml)-![](https://img.shields.io/github/stars/christophM/iml?style=social) | \"An R package that interprets the behavior and explains predictions of machine learning models.\"|\n| [ingredients](https://github.com/ModelOriented/ingredients)-![](https://img.shields.io/github/stars/ModelOriented/ingredients?style=social) | \"A collection of tools for assessment of feature importance and feature effects.\"|\n| [interpret: Fit Interpretable Machine Learning Models](https://cran.r-project.org/web/packages/interpret/index.html) | \"Package for training interpretable machine learning models.\"|\n| [lightgbmExplainer](https://github.com/lantanacamara/lightgbmExplainer)-![](https://img.shields.io/github/stars/lantanacamara/lightgbmExplainer?style=social) | \"An R package that makes LightGBM models fully interpretable.\"|\n| [lime](https://github.com/thomasp85/lime)-![](https://img.shields.io/github/stars/thomasp85/lime?style=social) | \"R port of the Python lime package.\"|\n| [live](https://cran.r-project.org/web/packages/live/index.html) | \"Helps to understand key factors that drive the decision made by complicated predictive model (black box model).\"|\n| [mcr](https://github.com/aaronjfisher/mcr)-![](https://img.shields.io/github/stars/aaronjfisher/mcr?style=social) | \"An R package for Model Reliance and Model Class Reliance.\"|\n| [modelDown](https://cran.r-project.org/web/packages/modelDown/index.html) | \"Website generator with HTML summaries for predictive models.\"|\n| [modelOriented](https://github.com/ModelOriented)-![](https://img.shields.io/github/stars/ModelOriented?style=social) | GitHub repositories of Warsaw-based MI².AI. |\n| [modelStudio](https://github.com/ModelOriented/modelStudio)-![](https://img.shields.io/github/stars/ModelOriented/modelStudio?style=social) | \"Automates the explanatory analysis of machine learning predictive models.\"|\n| [Monotonic](http://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html) [XGBoost](http://xgboost.readthedocs.io/en/latest/) | Enforces consistent, directional relationships between features and predicted outcomes, enhancing model performance by aligning with prior data expectations. |\n| [quantreg](https://cran.r-project.org/web/packages/quantreg/index.html) | \"Estimation and inference methods for models for conditional quantile functions.\" |\n| [rpart](https://cran.r-project.org/web/packages/rpart/index.html) | \"Recursive partitioning for classification, regression and survival trees.\" |\n| [RuleFit](http://statweb.stanford.edu/~jhf/R_RuleFit.html) | \"Implements the learning method and interpretational tools described in *Predictive Learning via Rule Ensembles*.\" |\n| [Scalable Bayesian Rule Lists -SBRL](https://users.cs.duke.edu/~cynthia/code/sbrl_1.0.tar.gz) | A more scalable implementation of Bayesian rule list from the Rudin group at Duke. |\n| [shapFlex](https://github.com/nredell/shapFlex)-![](https://img.shields.io/github/stars/nredell/shapFlex?style=social) | Computes stochastic Shapley values for machine learning models to interpret them and evaluate fairness, including causal constraints in the feature space. |\n| [shapleyR](https://github.com/redichh/ShapleyR)-![](https://img.shields.io/github/stars/redichh/ShapleyR?style=social) | \"An R package that provides some functionality to use mlr tasks and models to generate shapley values.\" |\n| [shapper](https://cran.r-project.org/web/packages/shapper/index.html) | \"Provides SHAP explanations of machine learning models.\" |\n| [smbinning](https://cran.r-project.org/web/packages/smbinning/index.html) | \"A set of functions to build a scoring model from beginning to end.\" |\n| [vip](https://github.com/koalaverse/vip)-![](https://img.shields.io/github/stars/koalaverse/vip?style=social) | \"An R package for constructing variable importance plots (VIPs).\" |\n| [xgboostExplainer](https://github.com/AppliedDataSciencePartners/xgboostExplainer)-![](https://img.shields.io/github/stars/AppliedDataSciencePartners/xgboostExplainer?style=social) | \"An R package that makes xgboost models fully interpretable. |\n\n### Archived\n\n#### Archived: Official Policy, Frameworks, and Guidance\n\nFor official government files pertaining to responsible AI practices that have been taken offline, we provide Wayback Machine mirror links below. If a document is still available on its original official domain, it can currently be found in its respective subsection above, although it may later be incorporated into this list. Documents may be removed for various reasons (whether political or through routine updates), but archiving them ensures they remain accessible for historical reference. If you're a researcher who finds a dead link to an older version of a government document or one that has altogether been deleted without comment, please feel free to submit a pull request drawing our attention to it and we'll consider it for inclusion. Where possible, we provide links to what appear to be the most recent URLs that governments may want the public to access.\n\n* [Artificial Intelligence and Worker Well-Being: Principles and Best Practices for Developers and Employers](https://web.archive.org/web/20250205182942/https://www.dol.gov/sites/dolgov/files/general/ai/AI-Principles-Best-Practices.pdf) | United States, Department of Labor, archived February 5, 2025\n* [Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, HTML](https://web.archive.org/web/20250119213350/https://www.whitehouse.gov/ostp/ai-bill-of-rights/) | United States, The White House, Office of Science and Technology Policy, October 4, 2022, archived January 20, 2025\n* [Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, PDF](https://web.archive.org/web/20250119213350/https://www.whitehouse.gov/ostp/ai-bill-of-rights/) | United States, The White House, Office of Science and Technology Policy, October 4, 2022, archived January 20, 2025\n* [CISA Roadmap for Artificial Intelligence 2023 2024](https://web.archive.org/web/20250221181621/https://www.cisa.gov/sites/default/files/2023-11/2023-2024_CISA-Roadmap-for-AI_508c.pdf) | United States, Cybersecurity and Infrastructure Security Agency, November 2023\n* [Data Availability and Transparency Act 2022](https://web.archive.org/web/20240314232025/https://www.datacommissioner.gov.au/law/dat-act)| Australia, Office of the National Data Commissioner, April 1, 2022, archived March 14, 2024\n* [Developing Financial Sector Resilience in a Digital World: Selected Themes in Technology and Related Risks](https://web.archive.org/web/20230802000841/https://www.osfi-bsif.gc.ca/Eng/Docs/tchrsk.pdf) | Canada, Office of the Superintendent of Financial Institutions of Canada, September 2020, archived August 2, 2023\n* [Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence](https://web.archive.org/web/20250120132537/https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/) | United States, The White House, October 30, 2023, archived January 20, 2025\n* [FACT SHEET: Biden-⁠Harris Administration Announces New AI Actions and Receives Additional Major Voluntary Commitment on AI](https://web.archive.org/web/20250120101805/https://www.whitehouse.gov/briefing-room/statements-releases/2024/07/26/fact-sheet-biden-harris-administration-announces-new-ai-actions-and-receives-additional-major-voluntary-commitment-on-ai/) | United States, The White House, July 26, 2024, archived January 20, 2025\n* [FACT SHEET: Biden-⁠Harris Administration Outlines Coordinated Approach to Harness Power of AI for U.S. National Security](https://web.archive.org/web/20250119050242/https://www.whitehouse.gov/briefing-room/statements-releases/2024/10/24/fact-sheet-biden-harris-administration-outlines-coordinated-approach-to-harness-power-of-ai-for-u-s-national-security/) | United States, The White House, October 24, 2024, archived January 19, 2025\n* [FACT SHEET: Biden-⁠Harris Administration Secures Voluntary Commitments from Leading Artificial Intelligence Companies to Manage the Risks Posed by AI](https://web.archive.org/web/20250120131235/https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/) | United States, The White House, July 21, 2023, archived January 20, 2025\n* [FACT SHEET: Biden-⁠Harris Administration Takes New Steps to Advance Responsible Artificial Intelligence Research, Development, and Deployment](https://web.archive.org/web/20250117044009/https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/23/fact-sheet-biden-harris-administration-takes-new-steps-to-advance-responsible-artificial-intelligence-research-development-and-deployment/) | United States, The White House, May 23, 2023, archived January 17, 2025\n* [FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence](https://web.archive.org/web/20250118214923/https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/) | United States, The White House, October 30, 2023, archived January 18, 2025\n* [Federal Register of Legislation, Data Availability and Transparency Act 2022](https://www.legislation.gov.au/C2022A00011/latest/text)\n* [Generative Artificial Intelligence Lexicon](https://web.archive.org/web/20240926203350/https://www.ai.mil/lexicon_ai_terms.html) | United States, Department of Defense, Chief Digital and Artificial Intelligence Office (CDAO), archived September 26, 2024\n* [Generative Artificial Intelligence Risk Assessment SIMM 5305-F](https://web.archive.org/web/20240524154534/https://cdt.ca.gov/wp-content/uploads/2024/03/SIMM-5305-F-Generative-Artificial-Intelligence-Risk-Assessment-FINAL.pdf) | State of California, Department of Technology, Office of Information Security, March 2024, archived May 24, 2024\n  * [Generative Artificial Intelligence Risk Assessment SIMM 5305-F February 2025 update](https://cdt.ca.gov/wp-content/uploads/2025/01/SIMM-5305-F-GenAI-Risk-Assessment-2025_0131-final.pdf)\n* [Guidelines on the Application of Republic Act No. 10173 or the Data Privacy Act of 2012 DPA, Its Implementing Rules and Regulations, and the Issuances of the Commission to Artificial Intelligence Systems Processing Personal Data NPC Advisory No. 2024-04](https://web.archive.org/web/20250112215325/https://privacy.gov.ph/wp-content/uploads/2024/12/Advisory-2024.12.19-Guidelines-on-Artificial-Intelligence-w-SGD.pdf) | Philippines, National Privacy Commission, December 19, 2024, archived January 12, 2025\n* [Introducing the DATA Scheme](https://www.datacommissioner.gov.au/the-data-scheme)\n* [M-21-06 Memorandum for the Heads of Executive Departments and Agencies, Guidance for Regulation of Artificial Intelligence Applications](https://web.archive.org/web/20250118013159/https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf) | United States, Executive Office of the President, Office of Management and Budget, November 17, 2020, archived January 18, 2025\n* [M-24-18 Memorandum for the Heads of Executive Departments and Agencies, Advancing the Responsible Acquisition of Artificial Intelligence in Government](https://web.archive.org/web/20250118023352/https://www.whitehouse.gov/wp-content/uploads/2024/10/M-24-18-AI-Acquisition-Memorandum.pdf) | United States, Executive Office of the President, Office of Management and Budget, September 24, 2024, archived January 18, 2025\n* [Memorandum on Advancing the United States’ Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence](https://web.archive.org/web/20250116072308/https://www.whitehouse.gov/briefing-room/presidential-actions/2024/10/24/memorandum-on-advancing-the-united-states-leadership-in-artificial-intelligence-harnessing-artificial-intelligence-to-fulfill-national-security-objectives-and-fostering-the-safety-security/) | United States, The White House, October 24, 2024, archived January 16, 2025\n* [National Artificial Intelligence Research and Development Strategic Plan 2023 Update](https://web.archive.org/web/20250116083052/https://www.whitehouse.gov/wp-content/uploads/2023/05/National-Artificial-Intelligence-Research-and-Development-Strategic-Plan-2023-Update.pdf) | United States, Executive Office of the President, National Science and Technology Council, Select Committee on Artificial Intelligence, May 2023, archived January 16, 2025\n* [National Science and Technology Council](https://web.archive.org/web/20250118020849/https://www.whitehouse.gov/ostp/ostps-teams/nstc/) | United States, The White House, Office of Science and Technology Policy, January 16, 2021, archived January 18, 2025\n* [Office of Science and Technology Policy](https://web.archive.org/web/20250120110259/https://www.whitehouse.gov/ostp/) | United States, The White House, Office of Science and Technology Policy, January 13, 2021, archived January 20, 2025\n* [Supervisory Guidance on Model Risk Management](https://www.fdic.gov/news/financial-institution-letters/2017/fil17022a.pdf) | ( United States, Federal Deposit Insurance Corporation, archived February 13, 2024\n * [Aiming for truth, fairness, and equity in your company’s use of AI](https://web.archive.org/web/20250117235232/https://www.ftc.gov/business-guidance/blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai) | United States, Federal Trade Commission, Elisa Jillson, April 19, 2021, archived January 17, 2025\n* [Using Artificial Intelligence and Algorithms](https://web.archive.org/web/20240115210007/https://www.ftc.gov/business-guidance/blog/2020/04/using-artificial-intelligence-and-algorithms) | United States, Federal Trade Commission, Andrew Smith, April 8, 2020, archived January 15, 2024\n* [Validation of Employee Selection Procedures](https://web.archive.org/web/20250103095140/https://www.dol.gov/agencies/ofccp/faqs/employee-selection-procedures) | Office of Federal Contract Compliance Programs (archived)\n\n### Citing Awesome Machine Learning Interpretability\n\nContributors with over 100 edits can be named coauthors in the citation of visible names. Otherwise, all contributors with fewer than 100 edits are included under \"et al.\"\n\n#### Bibtex\n\n```\n@misc{amli_repo,\n  author={Patrick Hall and Daniel Atherton},\n  title={Awesome Machine Learning Interpretability},\n  year={2024},\n  note={\\url{https://github.com/jphall663/awesome-machine-learning-interpretability}}\n}\n```\n\n#### ACM, APA, Chicago, and MLA\n\n* **ACM (Association for Computing Machinery)**\n\nHall, Patrick, Daniel Atherton, et al. 2024. Awesome Machine Learning Interpretability. GitHub. https://github.com/jphall663/awesome-machine-learning-interpretability.\n\n* **APA (American Psychological Association) 7th Edition**\n\nHall, Patrick, Daniel Atherton, et al. (2024). Awesome Machine Learning Interpretability [GitHub repository]. GitHub. https://github.com/jphall663/awesome-machine-learning-interpretability.\n\n* **Chicago Manual of Style 17th Edition**\n\nHall, Patrick, Daniel Atherton, et al. \"Awesome Machine Learning Interpretability.\" GitHub. Last modified 2023. https://github.com/jphall663/awesome-machine-learning-interpretability.\n\n* **MLA (Modern Language Association) 9th Edition**\n\nHall, Patrick, Daniel Atherton, et al. \"Awesome Machine Learning Interpretability.\" *GitHub*, 2024, https://github.com/jphall663/awesome-machine-learning-interpretability. Accessed 5 March 2024.\n"
  },
  {
    "path": "archive/README_04_2025.md.bak",
    "content": "# Awesome Machine Learning Interpretability [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA maintained and curated list of practical and awesome responsible machine learning resources.\n\nIf you want to contribute to this list (*and please do!*), read over the [contribution guidelines](contributing.md), send a [pull request](https://github.com/jphall663/awesome-machine-learning-interpretability/compare), or file an [issue](https://github.com/jphall663/awesome-machine-learning-interpretability/issues/new). \n\nIf something you contributed or found here is missing after our September 2023 reboot, please check the [archive](https://github.com/jphall663/awesome-machine-learning-interpretability/blob/master/archive/README.md.bak).\n\n![](HR-logo-350x100.png)\n\nMaintenance and curation sponsored by [HallResearch.ai](https://www.hallresearch.ai).\n\n## Contents\n\n* **Community and Official Guidance Resources** \n  * [Community Frameworks and Guidance](#community-frameworks-and-guidance)\n    * [Infographics and Cheat Sheets](#infographics-and-cheat-sheets)\n    * [AI Red-Teaming Resources](#ai-red-teaming-resources)\n    * [Generative AI Explainability](#generative-ai-explainability)\n    * [University Policies and Guidance](#university-policies-and-guidance)\n  * [Conferences and Workshops](#conferences-and-workshops)\n  * [Official Policy, Frameworks, and Guidance](#official-policy-frameworks-and-guidance)\n    * [Australia](#australia)\n    * [Brazil](#brazil)\n    * [Canada](#canada)\n    * [Colombia](#colombia)\n    * [Costa Rice](#costa-rica)\n    * [Denmark](#denmark)\n    * [Finland](#finland)\n    * [France](#france)\n    * [Germany](#germany)\n    * [Hong Kong](#hong-kong)\n    * [Iceland](#iceland)\n    * [Ireland](#ireland)\n    * [Jamaica](#jamaica)\n    * [Japan](#japan)\n    * [Malaysia](#malaysia)\n    * [Mexico](#mexico)\n    * [Moldova](#moldova)\n    * [Netherlands](#netherlands)\n    * [New Zealand](#new-zealand)\n    * [Norway](#norway)\n    * [Philippines](#philippines)\n    * [Singapore](#singapore)\n    * [South Korea](#south-korea)\n    * [Switzerland](#switzerland)\n    * [Ukraine](#ukraine)\n    * [United Kingdom](#united-kingdom)\n    * [United States (Federal Government)](#united-states-federal-government)\n    * [United States (State Governments)](#united-states-state-governments)\n    * [International and Multilateral Frameworks](#international-and-multilateral-frameworks)\n    * [European Union Policies and Regulations](#european-union-policies-and-regulations)\n      * [Council of Europe](#council-of-europe)\n      * [European Commission and Parliament](#european-commission-and-parliament)\n      * [European Council](#european-council)\n      * [European Data Protection Authorities](#european-data-protection-authorities)\n    * [OECD](#oecd)\n    * [OSCE](#osce)\n    * [NATO](#nato)\n    * [United Nations](#united-nations)\n  * [Law Texts and Drafts](#law-texts-and-drafts)\n\n* **Education Resources**\n  * [Comprehensive Software Examples and Tutorials](#comprehensive-software-examples-and-tutorials)\n  * [Free-ish Books](#free-ish-books)\n  * [Glossaries and Dictionaries](#glossaries-and-dictionaries)\n  * [Open-ish Classes](#open-ish-classes)\n  * [Podcasts and Channels](#podcasts-and-channels)\n\n* **AI Incidents, Critiques, and Research Resources**\n  * [AI Incident Information Sharing Resources](#ai-incident-information-sharing-resources)\n    * [Bibliography of Papers on AI Incidents and Failures](#bibliography-of-papers-on-ai-incidents-and-failures)\n  * [AI Law, Policy, and Guidance Trackers](#ai-law-policy-and-guidance-trackers)\n  * [Challenges and Competitions](#challenges-and-competitions)\n  * [Critiques of AI](#critiques-of-ai)\n    * [Environmental Costs of AI](#environmental-costs-of-ai)\n  * [Groups and Organizations](#groups-and-organizations)\n  * [Curated Bibliographies](#curated-bibliographies)\n  * [List of Lists](#list-of-lists)\n  * [Platforms](#platforms)\n\n* **Technical Resources**\n  * [Benchmarks](#benchmarks)\n  * [Common or Useful Datasets](#common-or-useful-datasets)\n  * [Domain-specific Software](#domain-specific-software)\n  * [Machine Learning Environment Management Tools](#machine-learning-environment-management-tools)\n  * [Personal Data Protection Tools](#personal-data-protection-tools)\n  * [Open Source/Access Responsible AI Software Packages](#open-sourceaccess-responsible-ai-software-packages)\n    * [Browser](#browser)\n    * [C/C++](#cc)\n    * [JavaScript](#javascript)\n    * [Python](#python)\n    * [R](#r)\n\n* **Archived**\n  * [Archived: Official Policy, Frameworks, and Guidance](#archived-official-policy-frameworks-and-guidance)\n   \n* **Citing Awesome Machine Learning Interpretability**\n  * [Citation](#citing-awesome-machine-learning-interpretability)\n\n## Community and Official Guidance Resources\n\n### Community Frameworks and Guidance \n\nThis section is for responsible ML guidance put forward by organizations or individuals, not for official government guidance.\n\n* [8 Principles of Responsible ML](https://ethical.institute/principles.html)\n* [A Brief Overview of AI Governance for Responsible Machine Learning Systems](https://arxiv.org/pdf/2211.13130.pdf)\n* [Acceptable Use Policies for Foundation Models](https://github.com/kklyman/aupsforfms)![](https://img.shields.io/github/stars/kklyman/aupsforfms?style=social)\n* [Access Now, Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report](https://www.accessnow.org/wp-content/uploads/2024/07/TRF-LAC-Reporte-Regional-IA-JUN-2024-V3.pdf)\n* [Ada Lovelace Institute, Code and Conduct: How to Create Third-Party Auditing Regimes for AI Systems](https://www.adalovelaceinstitute.org/report/code-conduct-ai/)\n* [Adversarial ML Threat Matrix](https://github.com/mitre/advmlthreatmatrix)![](https://img.shields.io/github/stars/mitre/advmlthreatmatrix?style=social)\n* AI Action Summit, January 2025 | [International AI Safety Report: The International Scientific Report on the Safety of Advanced AI](https://assets.publishing.service.gov.uk/media/679a0c48a77d250007d313ee/International_AI_Safety_Report_2025_accessible_f.pdf)\n* [AI Governance Needs Sociotechnical Expertise: Why the Humanities and Social Sciences Are Critical to Government Efforts](https://datasociety.net/wp-content/uploads/2024/05/DS_AI_Governance_Policy_Brief.pdf)\n* [AI Model Registries: A Foundational Tool for AI Governance, September 2024](https://arxiv.org/pdf/2410.09645)\n* [AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources](https://arxiv.org/pdf/2503.05780)\n* [AI Verify](https://aiverifyfoundation.sg/what-is-ai-verify/):\n  * [AI Verify Foundation](https://aiverifyfoundation.sg/what-is-ai-verify/)\n  * [AI Verify Foundation, Cataloguing LLM Evaluations](https://aiverifyfoundation.sg/downloads/Cataloguing_LLM_Evaluations.pdf)\n  * [AI Verify Foundation, Generative AI: Implications for Trust and Governance](https://aiverifyfoundation.sg/downloads/Discussion_Paper.pdf)\n  * [AI Verfiy Foundation, Model Governance Framework for Generative AI](https://aiverifyfoundation.sg/wp-content/uploads/2024/05/Model-AI-Governance-Framework-for-Generative-AI-May-2024-1-1.pdf)\n* [AI Snake Oil](https://www.aisnakeoil.com/)\n* [American Sunlight Project, Deepfake Pornography Goes to Washington: Measuring the Prevalence of AI-Generated Non-Consensual Intimate Imagery Targeting Congress, December 11, 2024](https://static1.squarespace.com/static/6612cbdfd9a9ce56ef931004/t/67586997eaec5c6ae3bb5e24/1733847451191/ASP+DFP+Report.pdf)\n* [The Alan Turing Institute, AI Ethics and Governance in Practice](https://www.turing.ac.uk/research/research-projects/ai-ethics-and-governance-practice)\n* [The Alan Turing Institute, Responsible Data Stewardship in Practice](https://www.turing.ac.uk/sites/default/files/2024-06/aieg-ati-5-datastewardshipv1.2.pdf)\n* [The Alan Turing Institute, AI Standards Hub](https://www.turing.ac.uk/research/research-projects/ai-standards-hub)\n* [Andreessen Horowitz (a16z) AI Canon](https://a16z.com/ai-canon/)\n* [AuditBoard: 5 AI Auditing Frameworks to Encourage Accountability](https://www.auditboard.com/blog/ai-auditing-frameworks/)\n* [Auditing machine learning algorithms: A white paper for public auditors](https://www.auditingalgorithms.net/index.html)\n* [AWS Data Privacy FAQ](https://aws.amazon.com/compliance/data-privacy-faq/)\n* [AWS Privacy Notice](https://aws.amazon.com/privacy/)\n* [AWS, What is Data Governance?](https://aws.amazon.com/what-is/data-governance/)\n* [Berryville Institute of Machine Learning, Architectural Risk Analysis of Large Language Models (requires free account login)](https://berryvilleiml.com/results/BIML-LLM24.pdf)\n* Bertelsmann Stiftung, December 2024 | [The AI Act between Digital and Sectoral Regulations](https://www.bertelsmann-stiftung.de/fileadmin/files/user_upload/The_AI_Act_between_Digital_and_Sectoral_Regulations__2024_en.pdf)\n* [BIML Interactive Machine Learning Risk Framework](https://berryvilleiml.com/interactive/)\n* [Boston University AI Task Force Report on Generative AI in Education and Research](https://www.bu.edu/hic/files/2024/04/BU-AI-Task-Force-Report.pdf)\n* [Brendan Bycroft's LLM Visualization](https://bbycroft.net/llm)\n* [Brown University, How Can We Tackle AI-Fueled Misinformation and Disinformation in Public Health?](https://www.bu.edu/ceid/2024/04/25/how-can-we-tackle-ai-fueled-misinformation-and-disinformation-in-public-health/)\n* [Casey Flores, AIGP Study Guide](https://www.linkedin.com/feed/update/urn:li:activity:7201048113090809856?utm_source=share&utm_medium=member_desktop)\n* Center for Security and Emerging Technology (CSET):\n  * [CSET's Harm Taxonomy for the AI Incident Database](https://github.com/georgetown-cset/CSET-AIID-harm-taxonomy)![](https://img.shields.io/github/stars/georgetown-cset/CSET-AIID-harm-taxonomy?style=social)\n  * [CSET Publications](https://cset.georgetown.edu/publications/)\n  * [Adding Structure to AI Harm: An Introduction to CSET's AI Harm Framework](https://cset.georgetown.edu/publication/adding-structure-to-ai-harm/)\n  * [AI Accidents: An Emerging Threat: What Could Happen and What to Do, CSET Policy Brief, July 2021](https://cset.georgetown.edu/wp-content/uploads/CSET-AI-Accidents-An-Emerging-Threat.pdf)\n  * [AI Incident Collection: An Observational Study of the Great AI Experiment](https://cset.georgetown.edu/publication/ai-incident-collection-an-observational-study-of-the-great-ai-experiment/)\n  * [Chinese Critiques of Large Language Models: Finding the Path to General Intelligence | January 2025](https://cset.georgetown.edu/wp-content/uploads/CSET-Chinese-Critiques-of-Large-Language-Models-Finding-the-Path-to-General-Artificial-Intelligence.pdf)\n  * [Repurposing the Wheel: Lessons for AI Standards](https://cset.georgetown.edu/publication/repurposing-the-wheel/)\n  * [Translating AI Risk Management Into Practice](https://cset.georgetown.edu/article/translating-ai-risk-management-into-practice/)\n  * [Understanding AI Harms: An Overview](https://cset.georgetown.edu/article/understanding-ai-harms-an-overview/)\n* [Censius, AI Audit](https://censius.ai/wiki/ai-audit)\n* [Censius, An In-Depth Guide To Help You Start Auditing Your AI Models](https://censius.ai/blogs/ai-audit-guide)\n* [Center for AI and Digital Policy Reports](https://www.caidp.org/reports/)\n* Center for AI Policy, February 2025 | [US Open-Source AI Governance: Balancing Ideological and Geopolitical Considerations with China Competition](https://cdn.prod.website-files.com/65af2088cac9fb1fb621091f/67aaca031ed677c879434284_Final_US%20Open-Source%20AI%20Governance.pdf)\n* [Center for Countering Digital Hate (CCDH), YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf)\n* Center for Democracy and Technology (CDT)\n  * [AI Policy & Governance](https://cdt.org/area-of-focus/ai-policy-governance/)\n  * [Applying Sociotechnical Approaches to AI Governance in Practice](https://cdt.org/insights/applying-sociotechnical-approaches-to-ai-governance-in-practice/)\n  * [Assessing AI: Surveying the Spectrum of Approaches to Understanding and Auditing AI Systems | January 2025](https://cdt.org/wp-content/uploads/2025/01/2025-01-15-CDT-AI-Gov-Lab-Auditing-AI-report.pdf)\n  * [In Deep Trouble: Surfacing Tech-Powered Sexual Harassment in K-12 Schools](https://cdt.org/insights/report-in-deep-trouble-surfacing-tech-powered-sexual-harassment-in-k-12-schools/)\n* [Centre for International Governance Innovation Publications](https://www.cigionline.org/publications/)\n* [Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing](https://dl.acm.org/doi/abs/10.1145/3351095.3372873)\n* [Coalition for Content Provenance and Authenticity (C2PA)](https://c2pa.org/)\n* Convergence Analysis, May 2024 | [2024 State of the AI Regulatory Landscape](https://drive.google.com/file/d/13gyYbBixU75QwFQDTku0AMIovbeTp9_g/view)\n* [Crowe LLP: Internal auditor's AI safety checklist](https://www.crowe.com/insights/asset/i/internal-auditors-ai-safety-checklist)\n* [Data Provenance Explorer](https://www.dataprovenance.org/)\n* [Data & Society, AI Red-Teaming Is Not a One-Stop Solution to AI Harms: Recommendations for Using Red-Teaming for AI Accountability](https://datasociety.net/wp-content/uploads/2023/10/Recommendations-for-Using-Red-Teaming-for-AI-Accountability-PolicyBrief.pdf)\n* [Dealing with Bias and Fairness in AI/ML/Data Science Systems](https://docs.google.com/presentation/d/17o_NzplYua5fcJFuGcy1V1-5GFAHk7oHAF4dN44NkUE)\n* [Debugging Machine Learning Models (ICLR workshop proceedings)](https://debug-ml-iclr2019.github.io/)\n* [Demos, AI – Trustworthy By Design: How to build trust in AI systems, the institutions that create them and the communities that use them](https://demos.co.uk/research/ai-trustworthy-by-design-how-to-build-trust-in-ai-systems-the-institutions-that-create-them-and-the-communities-that-use-them/)\n* [Digital Policy Alert, The Anatomy of AI Rules: A systematic comparison of AI rules across the globe](https://digitalpolicyalert.org/ai-rules/the-anatomy-of-AI-rules)\n* [Distill](https://distill.pub)\n* [Dominique Shelton Leipzig, Countries With Draft AI Legislation or Frameworks](https://dominiquesheltonleipzig.com/country-legislation-frameworks/)\n* ECP Platform voor de InformatieSamenleving, November 2018 | [Artificial Intelligence Impact Assessment](https://ecp.nl/wp-content/uploads/2018/11/Artificial-Intelligence-Impact-Assesment.pdf)\n* [Ethical and social risks of harm from Language Models](https://www.deepmind.com/publications/ethical-and-social-risks-of-harm-from-language-models)\n* [Ethics for people who work in tech](https://ethicsforpeoplewhoworkintech.com/)\n* [The Ethics of AI Ethics: An Evaluation of Guidelines](https://link.springer.com/content/pdf/10.1007/s11023-020-09517-8.pdf)\n* [The Ethics of Developing, Implementing, and Using Advanced Warehouse Technologies: Top-Down Principles Versus The Guidance Ethics Approach](https://journals.open.tudelft.nl/jhtr/article/view/7098/6136)\n* [European Law Institute, Guidelines on the Application of the Definition of an AI System in the AI Act: ELI Proposal for a Three-Factor Approach (Response of the ELI to the EU Commission's Consultation), November 1, 2024](https://www.europeanlawinstitute.eu/fileadmin/user_upload/p_eli/Publications/ELI_Response_on_the_definition_of_an_AI_System.pdf)\n* [Evaluating LLMs is a minefield](https://www.cs.princeton.edu/~arvindn/talks/evaluating_llms_minefield/)\n* [Fairly's Global AI Regulations Map](https://github.com/fairlyAI/global-ai-regulations-map/blob/dev/README.md)![](https://img.shields.io/github/stars/fairlyAI/global-ai-regulations-map?style=social)\n* [Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey](https://dl.acm.org/doi/10.1145/3696457)\n* [FATML Principles and Best Practices](https://www.fatml.org/resources/principles-and-best-practices)\n* [Federation of American Scientists, A NIST Foundation To Support The Agency’s AI Mandate](https://fas.org/publication/nist-foundation/)\n* [Financial Industry Regulatory Authority (FINRA), Artificial Intelligence (AI) in the Securities Industry](https://www.finra.org/sites/default/files/2020-06/ai-report-061020.pdf)\n* [ForHumanity Body of Knowledge (BOK)](https://forhumanity.center/bok/)\n* [The Foundation Model Transparency Index](https://crfm.stanford.edu/fmti/)\n  * [Trustible, Model Transparency Ratings](https://aimodelratings.com/)\n* [From Principles to Practice: An interdisciplinary framework to operationalise AI ethics](https://www.ai-ethics-impact.org/resource/blob/1961130/c6db9894ee73aefa489d6249f5ee2b9f/aieig---report---download-hb-data.pdf)\n* [FS-ISAC, February 2024, Generative AI Vendor Risk Assessment Guide](https://www.fsisac.com/hubfs/Knowledge/AI/FSISAC_GenerativeAI-VendorEvaluation&QualitativeRiskAssessment.pdf)\n* [The Future Society](https://thefuturesociety.org/towards-effective-governance-of-foundation-models-and-generative-ai/)\n* [Gage Repeatability and Reproducibility](https://asq.org/quality-resources/gage-repeatability)\n* Google:\n  * [The Data Cards Playbook](https://sites.research.google/datacardsplaybook/)\n  * [Data governance in the cloud - part 1 - People and processes](https://cloud.google.com/blog/products/data-analytics/data-governance-and-operating-model-for-analytics-pt1)\n  * [Data Governance in the Cloud - part 2 - Tools](https://cloud.google.com/blog/products/data-analytics/data-governance-in-the-cloud-part-2-tools)\n  * [Evaluating social and ethical risks from generative AI](https://deepmind.google/discover/blog/evaluating-social-and-ethical-risks-from-generative-ai/)\n  * [Generative AI Prohibited Use Policy](https://policies.google.com/terms/generative-ai/use-policy)\n  * [Perspectives on Issues in AI Governance](https://ai.google/static/documents/perspectives-on-issues-in-ai-governance.pdf)\n  * [Principles and best practices for data governance in the cloud](https://services.google.com/fh/files/misc/principles_best_practices_for_data-governance.pdf)\n  * [Responsible AI Framework](https://cloud.google.com/responsible-ai)\n  * [Responsible AI practices](https://ai.google/responsibility/responsible-ai-practices/)\n  * [Testing and Debugging in Machine Learning](https://developers.google.com/machine-learning/testing-debugging)\n* [GSMA, September 2024, Best Practice Tools: Examples supporting responsible AI maturity](https://www.gsma.com/solutions-and-impact/connectivity-for-good/external-affairs/wp-content/uploads/2024/09/GSMA-ai4i_Best-Practice-Tools_v7.pdf)\n* [HackerOne Blog](https://www.hackerone.com/vulnerability-and-security-testing-blog)\n* [Haptic Networks: How to Perform an AI Audit for UK Organisations](https://www.haptic-networks.com/cyber-security/how-to-perform-an-ai-audit/)\n* [Hogan Lovells, The AI Act is coming: EU reaches political agreement on comprehensive regulation of artificial intelligence](https://www.engage.hoganlovells.com/knowledgeservices/news/the-ai-act-is-coming-eu-reaches-political-agreement-on-comprehensive-regulation-of-artificial-intelligence?nav=FRbANEucS95NMLRN47z%2BeeOgEFCt8EGQ71hKXzqW2Ec%3D&key=BcJlhLtdCv6%2FJTDZxvL23TQa3JHL2AIGr93BnQjo2SkGJpG9xDX7S2thDpAQsCconWHAwe6cJTmX%2FZxLGrXbZz2L%2BEiiz68X&uid=iZAX%2FROFT6Q%3D)\n* [Hugging Face, The Landscape of ML Documentation Tools](https://huggingface.co/docs/hub/model-card-landscape-analysis)\n* [IAPP, Global AI Governance Law and Policy: Canada, EU, Singapore, UK and US](https://iapp.org/media/pdf/resource_center/global_ai_governance_law_policy_series.pdf)\n* [IBM, AI ethics in action: An enterprise guide to progressing trustworthy AI](https://www.ibm.com/downloads/documents/us-en/10c31775c6d400ed)\n* [IBM, Design for AI](https://www.ibm.com/design/ai/fundamentals/)\n* [IBM, Principles and Practices for Building More Trustworthy AI, October 6, 2021](https://newsroom.ibm.com/Principles-and-Practices-for-Building-More-Trustworthy-AI)\n* [ICT Institute: A checklist for auditing AI systems](https://ictinstitute.nl/a-checklist-for-auditing-ai-systems/)\n* IEEE: \n  * [A Flexible Maturity Model for AI Governance Based on the NIST AI Risk Management Framework](https://ieeeusa.org/product/a-flexible-maturity-model-for-ai-governance/)\n  * [The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, General Principles](https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_general_principles_v2.pdf)\n  * [An Overview of Artificial Intelligence Ethics](https://ieeexplore.ieee.org/document/9844014)\n  * [P3119 Standard for the Procurement of Artificial Intelligence and Automated Decision Systems](https://standards.ieee.org/ieee/3119/10729/)  \n  * [Std 1012-1998 Standard for Software Verification and Validation](https://people.eecs.ku.edu/~hossein/Teaching/Stds/1012.pdf)\n* [Independent Audit of AI Systems](https://forhumanity.center/independent-audit-of-ai-systems/)\n* [Identifying and Overcoming Common Data Mining Mistakes](https://support.sas.com/resources/papers/proceedings/proceedings/forum2007/073-2007.pdf)\n* [Infocomm Media Development Authority (Singapore) and AI Verify Foundation, Cataloguing LLM Evaluations, Draft for Discussion (October 2023)](https://aiverifyfoundation.sg/downloads/Cataloguing_LLM_Evaluations.pdf)\n* [Infocomm Media Development Authority (Singapore), First of its kind Generative AI Evaluation Sandbox for Trusted AI by AI Verify Foundation and IMDA](https://www.imda.gov.sg/resources/press-releases-factsheets-and-speeches/press-releases/2023/generative-ai-evaluation-sandbox)\n* [Information Technology Industry (ITI) Council, October 2024, ITI's AI Security Policy Principles](https://www.itic.org/documents/artificial-intelligence/ITI_AI-Security-Principles_102124_FINAL.pdf)\n* [International Bar Association and the Center for AI and Digital Policy, The Future Is Now: Artificial Intelligence and the Legal Profession](https://www.ibanet.org/document?id=The-future-is%20now-AI-and-the-legal-profession-report)\n* [Institute for AI Policy and Strategy (IAPS), AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies](https://www.iaps.ai/research/ai-relevant-regulatory-precedent)\n* [Institute for AI Policy and Strategy (IAPS), Key questions for the International Network of AI Safety Institutes](https://www.iaps.ai/research/international-network-aisis)\n* [Institute for AI Policy and Strategy (IAPS), Mapping Technical Safety Research at AI Companies: A literature review and incentives analysis](https://arxiv.org/pdf/2409.07878)\n* [Institute for AI Policy and Strategy (IAPS), Understanding the First Wave of AI Safety Institutes: Characteristics, Functions, and Challenges](https://www.iaps.ai/research/understanding-aisis)\n* [Institute for Public Policy Research (IPPR), Transformed by AI: How Generative Artificial Intelligence Could Affect Work in the UK—And How to Manage It](https://ippr-org.files.svdcdn.com/production/Downloads/Transformed_by_AI_March24_2024-03-27-121003_kxis.pdf)\n* [Institute for Security and Technology (IST), The Implications of Artificial Intelligence in Cybersecurity: Shifting the Offense-Defense Balance](https://securityandtechnology.org/virtual-library/reports/the-implications-of-artificial-intelligence-in-cybersecurity/)\n* [Institute of Internal Auditors](https://www.theiia.org/en/pages/search-results/?keyword=artificial+intelligence)\n* [Inter-Parliamentary Union, Guidelines for AI in parliaments, December 2024](https://www.ipu.org/file/20632/download)\n* ISACA:\n  * [ISACA: Auditing Artificial Intelligence](https://ec.europa.eu/futurium/en/system/files/ged/auditing-artificial-intelligence.pdf)\n  * [ISACA: Auditing Guidelines for Artificial Intelligence](https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2020/volume-26/auditing-guidelines-for-artificial-intelligence)\n  * [ISACA: Capability Maturity Model Integration Resources](https://cmmiinstitute.com/)\n* [Integrity Institute Report, February 2024, On Risk Assessment and Mitigation for Algorithmic Systems](https://drive.google.com/file/d/1ZMt7igUcKUq00yakCnbxBCcaA7vajAix/view)\n* [International Monetary Fund, Gen-AI: Artificial Intelligence and the Future of Work](https://www.imf.org/-/media/Files/Publications/SDN/2024/English/SDNEA2024001.ashx)\n* [ISO/IEC 42001:2023, Information technology — Artificial intelligence — Management system](https://www.iso.org/standard/81230.html)\n* [Knowledge Centre Data & Society, Implementing the AI Act in Belgium: Scope of Application and Authorities, December 2024](https://data-en-maatschappij.ai/uploads/Policy-brief-Implementing-the-AI-act-in-Belgium_2024-12-23-115650_shpg.pdf)\n* [Know Your Data](https://knowyourdata.withgoogle.com/)\n* [Language Model Risk Cards: Starter Set](https://github.com/leondz/lm_risk_cards)![](https://img.shields.io/github/stars/leondz/lm_risk_cards?style=social)\n* [Large language models, ed with a minimum of math and jargon](https://www.understandingai.org/p/large-language-models-ed-with)\n* [Larry G. Wlosinski, April 30, 2021, Information System Contingency Planning Guidance](https://www.isaca.org/resources/isaca-journal/issues/2021/volume-3/information-system-contingency-planning-guidance)\n* [Library of Congress, LC Labs AI Planning Framework](https://github.com/LibraryOfCongress/labs-ai-framework)![](https://img.shields.io/github/stars/LibraryOfCongress/labs-ai-framework?style=social)\n* [Llama 2 Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/)\n* [LLM Visualization](https://bbycroft.net/llm)\n* [Machine Learning Quick Reference: Algorithms](https://support.sas.com/rnd/app/data-mining/enterprise-miner/pdfs/Machine_Learning_Quick_Ref_Algorithms_Mar2017.pdf)\n* [Machine Learning Quick Reference: Best Practices](https://support.sas.com/rnd/app/data-mining/enterprise-miner/pdfs/Machine_Learning_Quick_Ref_Best_Practices.pdf)\n* [Manifest MLBOM Wiki](https://github.com/manifest-cyber/mlbom)\n  * [Towards Traceability in Data Ecosystems using a Bill of Materials Model](https://arxiv.org/pdf/1904.04253.pdf)\n* Meta:\n  * [System cards](https://ai.meta.com/tools/system-cards/)\n* Microsoft:\n  * [Advancing AI responsibly](https://unlocked.microsoft.com/responsible-ai/)\n  * [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety)\n     * [Harm categories in Azure AI Content Safety](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/harm-categories?tabs=warning)\n     * [Microsoft Responsible AI Standard, v2](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE5cmFl)\n  * [GDPR and Generative AI: A Guide for Public Sector Organizations](https://wwps.microsoft.com/blog/gdpr-genai)\n  * [How Microsoft names threat actors](https://learn.microsoft.com/en-us/unified-secops-platform/microsoft-threat-actor-naming)\n* [MITRE, AI Assurance: A Repeatable Process for Assuring AI-enabled Systems, June 2024](https://www.mitre.org/sites/default/files/2024-06/PR-24-1768-AI-Assurance-A-Repeatable-Process-Assuring-AI-Enabled-Systems.pdf)\n* [MLA, How do I cite generative AI in MLA style?](https://style.mla.org/citing-generative-ai/)\n* [model-cards-and-datasheets](https://github.com/ivylee/model-cards-and-datasheets)![](https://img.shields.io/github/stars/ivylee/model-cards-and-datasheets?style=social)\n* [Network Contagion Research Institute (NCRI) | A Digital Pandemic: Uncovering the Role of 'Yahoo Boys' in the Surge of Social Media-Enabled Financial Sextortion Targeting Minors | January 2024](https://networkcontagion.us/wp-content/uploads/Yahoo-Boys_1.2.24.pdf)\n* [NewsGuard AI Tracking Center](https://www.newsguardtech.com/special-reports/ai-tracking-center/)\n* [OpenAI, Building an early warning system for LLM-aided biological threat creation](https://openai.com/research/building-an-early-warning-system-for-llm-aided-biological-threat-creation)\n* [OpenAI Cookbook, How to implement LLM guardrails](https://cookbook.openai.com/examples/how_to_use_guardrails)\n* [OpenAI, Evals](https://github.com/openai/evals)![](https://img.shields.io/github/stars/openai/evals?style=social)\n* [Open Data Institute, Understanding data governance in AI: Mapping governance](https://theodi.org/insights/reports/understanding-data-governance-in-ai-mapping-governance/)\n* [Open Sourcing Highly Capable Foundation Models](https://www.governance.ai/research-paper/open-sourcing-highly-capable-foundation-models)\n* [Organization and Training of a Cyber Security Team](http://ieeexplore.ieee.org/document/1245662)\n* [Our Data Our Selves, Data Use Policy](https://ourdataourselves.tacticaltech.org/data-use-policy/)\n* [OWASP, Guide for Preparing and Responding to Deepfake Events: From the OWASP Top 10 for LLM Applications Team, Version 1, September 2024](https://genai.owasp.org/resource/guide-for-preparing-and-responding-to-deepfake-events/)\n* [Oxford Commission on AI & Good Governance, AI in the Public Service: From Principles to Practice](https://oxcaigg.oii.ox.ac.uk/wp-content/uploads/sites/11/2021/12/AI-in-the-Public-Service-Final.pdf)\n* [PAIR Explorables: Datasets Have Worldviews](https://pair.withgoogle.com/explorables/dataset-worldviews/)\n* Paris Peace Forum, February 2025 | [Forging Global Cooperation on AI Risks: Cyber Policy as a Governance Blueprint](https://parispeaceforum.org/app/uploads/2025/02/forging-global-cooperation-on-ai-risks-cyber-policy-as-a-governance-blueprint.pdf)\n* [Partnership on AI, ABOUT ML Reference Document](https://partnershiponai.org/paper/about-ml-reference-document/)\n* [Partnership on AI, PAI’s Guidance for Safe Foundation Model Deployment: A Framework for Collective Action](https://partnershiponai.org/modeldeployment/)\n* [Partnership on AI, Responsible Practices for Synthetic Media: A Framework for Collective Action](https://syntheticmedia.partnershiponai.org/)\n* [Partnership on AI and Thorn, Mitigating the risk of generative AI models creating Child Sxual Abuse Materials: An analysis by child safety nonprofit Thorn](https://partnershiponai.org/wp-content/uploads/2024/11/case-study-thorn.pdf)\n* [PwC's Responsible AI](https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai.html)\n* RAND Corporation, U.S. Tort Liability for Large-Scale Artificial Intelligence Damages (broken/missing link)\n* [A Primer for Developers and Policymakers](https://www.rand.org/pubs/research_reports/RRA3084-1.html)\n* [RAND Corporation, Analyzing Harms from AI-Generated Images and Safeguarding Online Authenticity](https://www.rand.org/pubs/perspectives/PEA3131-1.html)\n* [Ravit Dotan's Projects](https://www.techbetter.ai/projects-1)\n* Responsible AI Institute | [AI Inventories: Practical Challenges for Organizational Risk Management](https://20965052.fs1.hubspotusercontent-na1.net/hubfs/20965052/AI%20Inventories%20Practical%20Challenges%20for%20Organizational%20Risk%20Management%20(3).pdf)\n* [Safe and Reliable Machine Learning](https://www.dropbox.com/s/sdu26h96bc0f4l7/FAT19-AI-Reliability-Final.pdf?dl=0)\n* [Sample AI Incident Response Checklist](https://bnh-ai.github.io/resources/)\n* [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html)\n* [SHRM Generative Artificial Intelligence (AI) Chatbot Usage Policy](https://www.shrm.org/resourcesandtools/tools-and-samples/policies/pages/chatgpt-generative-ai-usage.aspx)\n* Simon Institute for Longterm Governance, February 2025 | [Recommendations for the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance](https://drive.google.com/file/d/17mBzqt7foXThI9xcAP8gsTKan34Zk5Mv/view)\n* [Special Competitive Studies Project and Johns Hopkins University Applied Physics Laboratory, Framework for Identifying Highly Consequential AI Use Cases](https://www.scsp.ai/wp-content/uploads/2023/11/SCSP_JHU-HCAI-Framework-Nov-6.pdf)\n* [Stanford University Human-Centered Artificial Intelligence (HAI), Assessing the Implementation of Federal AI Leadership and Compliance Mandates](https://hai.stanford.edu/sites/default/files/2025-01/HAI-RegLab-White-Paper-Federal-AI-Leadership-and-Compliance.pdf)\n* [Stanford University, Open Problems in Technical AI Governance: A repository of open problems in technical AI governance](https://taig.stanford.edu/)\n* [Stanford University, Responsible AI at Stanford: Enabling innovation through AI best practices](https://uit.stanford.edu/security/responsibleai)\n* [Synack, The Complete Guide to Crowdsourced Security Testing, Government Edition](https://www.synack.com/wp-content/uploads/2022/09/Crowdsourced-Security-Landscape-Government.pdf)\n* [The Rise of Generative AI and the Coming Era of Social Media Manipulation 3.0: Next-Generation Chinese Astroturfing and Coping with Ubiquitous AI](https://www.rand.org/pubs/perspectives/PEA2679-1.html)\n* [Taskade: AI Audit PBC Request Checklist Template](https://www.taskade.com/templates/engineering/audit-pbc-request-checklist)\n* [Taylor & Francis, AI Policy](https://taylorandfrancis.com/our-policies/ai-policy/)\n* [Tech Policy Press - Artificial Intelligence](https://www.techpolicy.press/category/artificial-intelligence/)\n* [TechTarget: 9 questions to ask when auditing your AI systems](https://www.techrepublic.com/article/9-questions-to-ask-when-auditing-your-ai-systems/)\n* [Toward an evaluation science for generative AI systems](https://arxiv.org/pdf/2503.05336)\n* [Troubleshooting Deep Neural Networks](http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf)\n* [Trustible, Enhancing the Effectiveness of AI Governance Committees](https://www.trustible.ai/post/enhancing-the-effectiveness-of-ai-governance-committees)\n* [Twitter Algorithmic Bias Bounty](https://hackerone.com/twitter-algorithmic-bias?type=team)\n* [Unite.AI: How to perform an AI Audit in 2023](https://www.unite.ai/how-to-perform-an-ai-audit-in-2023/)\n* University of California, Berkeley, Center for Long-Term Cybersecurity\n  * Version 1.1, January 2025 | [AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models](https://cltc.berkeley.edu/wp-content/uploads/2025/01/Berkeley-AI-Risk-Management-Standards-Profile-for-General-Purpose-AI-and-Foundation-Models-v1-1.pdf)\n  * [Decision Points in AI Governance: Three Case Studies Explore Efforts to Operationalize AI Principles](https://cltc.berkeley.edu/wp-content/uploads/2020/05/Decision_Points_AI_Governance.pdf)\n  * February 2025 | [Intolerable Risk Threshold Recommendations for Artificial Intelligence: Key Principles, Considerations, and Case Studies to Inform Frontier AI Safety Frameworks for Industry and Government](https://cltc.berkeley.edu/wp-content/uploads/2025/02/Intolerable-Risk-Threshold-Recommendations-for-Artificial-Intelligence.pdf)\n  * January 2023 | [A Taxonomy of Trustworthiness for Artificial Intelligence](https://cltc.berkeley.edu/wp-content/uploads/2023/12/Taxonomy_of_AI_Trustworthiness_tables.pdf)\n* [University of California, Berkeley, Information Security Office, How to Write an Effective Website Privacy Statement](https://security.berkeley.edu/how-write-effective-website-privacy-statement)\n* [University of Washington Tech Policy Lab, Data Statements](https://techpolicylab.uw.edu/data-statements/)\n* [Warning Signs: The Future of Privacy and Security in an Age of Machine Learning](https://fpf.org/wp-content/uploads/2019/09/FPF_WarningSigns_Report.pdf)\n* [When Not to Trust Your Explanations](https://docs.google.com/presentation/d/10a0PNKwoV3a1XChzvY-T1mWudtzUIZi3sCMzVwGSYfM/edit)\n* [Who Should Develop Which AI Evaluations?](https://oms-www.files.svdcdn.com/production/downloads/reports/Who%20should%20develop%20which%20AI%20evaluations.pdf?dm=1737016728)\n* [Why We Need to Know More: Exploring the State of AI Incident Documentation Practices](https://dl.acm.org/doi/fullHtml/10.1145/3600211.3604700)\n* [WilmerHale, What Are High-Risk AI Systems Within the Meaning of the EU’s AI Act, and What Requirements Apply to Them?](https://www.wilmerhale.com/en/insights/blogs/wilmerhale-privacy-and-cybersecurity-law/20240717-what-are-highrisk-ai-systems-within-the-meaning-of-the-eus-ai-act-and-what-requirements-apply-to-them)\n* [World Economic Forum, AI Value Alignment: Guiding Artificial Intelligence Towards Shared Human Goals](https://www.weforum.org/publications/ai-value-alignment-guiding-artificial-intelligence-towards-shared-human-goals/)\n* [World Economic Forum, Responsible AI Playbook for Investors](https://www.weforum.org/publications/responsible-ai-playbook-for-investors/)\n* [World Privacy Forum, AI Governance on the Ground: Canada’s Algorithmic Impact Assessment Process and Algorithm has evolved](https://www.worldprivacyforum.org/2024/08/ai-governance-on-the-ground-series-canada/)\n* [World Privacy Forum, Risky Analysis: Assessing and Improving AI Governance Tools](https://www.worldprivacyforum.org/wp-content/uploads/2023/12/WPF_Risky_Analysis_December_2023_fs.pdf)\n* [World Economic Forum and Capgemini, Navigating the AI Frontier: A Primer on the Evolution and Impact of AI Agents, White Paper, December 2024](https://reports.weforum.org/docs/WEF_Navigating_the_AI_Frontier_2024.pdf)\n* [Worldwide AI Ethics: A Review of 200 Guidelines and Recommendations for AI Governance](https://arxiv.org/pdf/2206.11922)\n* [You Created A Machine Learning Application Now Make Sure It's Secure](https://www.oreilly.com/ideas/you-created-a-machine-learning-application-now-make-sure-its-secure)\n\n#### Infographics and Cheat Sheets\n\n* [AppliedAI Institute, Navigating the EU AI Act: A Process Map for making AI Systems available](https://www.appliedai-institute.de/assets/files/EU_AI_Act_Compliance_Journey.pdf)\n* [Foundation Model Development Cheatsheet](https://fmcheatsheet.org/)\n* [Future of Privacy Forum, EU AI Act: A Comprehensive Implementation & Compliance Timeline](https://fpf.org/resource/eu-ai-act-a-comprehensive-implementation-compliance-timeline/)\n* [Future of Privacy Forum, The Spectrum of Artificial Intelligence](https://fpf.org/wp-content/uploads/2021/01/FPF_AIEcosystem_illo_03.pdf)\n* [Generative AI framework and Generative AI value tree modelling diagram](https://media.licdn.com/dms/image/v2/D4D22AQEKqP2a6_rsCw/feedshare-shrink_1280/B4DZP0cUWFHUAo-/0/1734972885448?e=1738195200&v=beta&t=PMJq6Ti1lisMMkyhnWojcdDt_DAlmYtV6MUQbqWu4hc)\n* [Global Index for AI Safety: AGILE Index on Global AI Safety Readiness Feb 2025](https://agile-index.ai/Global-Index-For-AI-Safety-Report-EN.pdf)\n* [IAPP EU AI Act Cheat Sheet](https://iapp.org/media/pdf/resource_center/eu_ai_act_cheat_sheet.pdf)\n* [IAPP, EU AI Act Compliance Matrix](https://iapp.org/resources/article/eu-ai-act-compliance-matrix/)\n* [Machine Learning Attack_Cheat_Sheet](https://resources.oreilly.com/examples/0636920415947/-/blob/master/Attack_Cheat_Sheet.png)\n* [Oliver Patel's Cheat Sheets](https://www.linkedin.com/in/oliver-patel/recent-activity/images/)\n\n#### AI Red-Teaming Resources\n\n##### Papers\n* [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://arxiv.org/pdf/2411.00640)\n* [Exploiting Novel GPT-4 APIs](https://arxiv.org/abs/2312.14302)\n* [Identifying and Eliminating CSAM in Generative ML Training Data and Models](https://purl.stanford.edu/kh752sm9123)\n* [Jailbreaking Black Box Large Language Models in Twenty Queries](https://arxiv.org/abs/2310.08419)\n* [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://arxiv.org/abs/2404.08144)\n  * [No, LLM Agents can not Autonomously Exploit One-day Vulnerabilities](https://struct.github.io/auto_agents_1_day.html)\n* [Ofcom, Red Teaming for GenAI Harms: Revealing the Risks and Rewards for Online Safety, July 23, 2024](https://www.ofcom.org.uk/siteassets/resources/documents/consultations/discussion-papers/red-teaming/red-teaming-for-gen-ai-harms.pdf?v=370762)\n* OWASP Version 1.0, January 23, 2025 | [GenAI Red Teaming Guide: A Practical Approach to Evaluating AI Vulnerabilities](https://genai.owasp.org/download/44859/?tmstv=1737593350)\n* [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://arxiv.org/abs/2209.07858)\n* [Red Teaming of Advanced Information Assurance Concepts](https://ieeexplore.ieee.org/document/821513)\n\n##### Tools and Guidance\n* [@dotey on X/Twitter exploring GPT prompt security and prevention measures](https://x.com/dotey/status/1724623497438155031?s=20)\n* [0xeb / GPT-analyst](https://github.com/0xeb/gpt-analyst/)![](https://img.shields.io/github/stars/0xeb/gpt-analyst?style=social)\n* [0xk1h0 / ChatGPT \"DAN\" (and other \"Jailbreaks\")](https://github.com/0xk1h0/ChatGPT_DAN)![](https://github.com/0xk1h0/ChatGPT_DAN)![](https://img.shields.io/github/stars/0xk1h0/ChatGPT_DAN?style=social)\n* [ACL 2024 Tutorial: Vulnerabilities of Large Language Models to Adversarial Attacks](https://llm-vulnerability.github.io/)\n* [Azure's PyRIT](https://github.com/Azure/PyRIT)![](https://img.shields.io/github/stars/Azure/PyRIT?style=social)\n* [Berkeley Center for Long-Term Cybersecurity (CLTC), https://cltc.berkeley.edu/publication/benchmark-early-and-red-team-often-a-framework-for-assessing-and-managing-dual-use-hazards-of-ai-foundation-models/](https://cltc.berkeley.edu/publication/benchmark-early-and-red-team-often-a-framework-for-assessing-and-managing-dual-use-hazards-of-ai-foundation-models/)\n* [CDAO frameworks, guidance, and best practices for AI test & evaluation](https://gitlab.jatic.net/home/frameworks)\n* [ChatGPT_system_prompt](https://github.com/LouisShark/chatgpt_system_prompt)![](https://img.shields.io/github/stars/LouisShark/chatgpt_system_prompt?style=social)\n* [coolaj86 / Chat GPT \"DAN\" (and other \"Jailbreaks\")](https://gist.github.com/coolaj86/6f4f7b30129b0251f61fa7baaa881516)![](https://img.shields.io/github/stars/coolaj86?style=social)\n* [CSET, What Does AI-Red Teaming Actually Mean?](https://cset.georgetown.edu/article/what-does-ai-red-teaming-actually-mean/)\n* [DAIR Prompt Engineering Guide](https://www.promptingguide.ai/)\n  * [DAIR Prompt Engineering Guide GitHub](https://github.com/dair-ai/Prompt-Engineering-Guide)![](https://img.shields.io/github/stars/dair-ai/Prompt-Engineering-Guide?style=social)\n* [Extracting Training Data from ChatGPT](https://not-just-memorization.github.io/extracting-training-data-from-chatgpt.html)\n* [Frontier Model Forum: What is Red Teaming?](https://www.frontiermodelforum.org/uploads/2023/10/FMF-AI-Red-Teaming.pdf)\n* [Generative AI Red Teaming Challenge: Transparency Report 2024](https://drive.google.com/file/d/1JqpbIP6DNomkb32umLoiEPombK2-0Rc-/view)\n* [HackerOne, An Emerging Playbook for AI Red Teaming with HackerOne](https://www.hackerone.com/thought-leadership/ai-safety-red-teaming)\n* [Humane Intelligence, SeedAI, and DEFCON AI Village, Generative AI Red Teaming Challenge: Transparency Report 2024](https://drive.google.com/file/d/1JqpbIP6DNomkb32umLoiEPombK2-0Rc-/view)\n* [In-The-Wild Jailbreak Prompts on LLMs](https://github.com/verazuo/jailbreak_llms)![](https://img.shields.io/github/stars/verazuo/jailbreak_llms?style=social)\n* [leeky: Leakage/contamination testing for black box language models](https://github.com/mjbommar/leeky)![](https://img.shields.io/github/stars/mjbommar/leeky?style=social)\n* [LLM Security & Privacy](https://github.com/chawins/llm-sp)![](https://img.shields.io/github/stars/chawins/llm-sp?style=social)\n* [Membership Inference Attacks and Defenses on Machine Learning Models Literature](https://github.com/HongshengHu/membership-inference-machine-learning-literature)![](https://img.shields.io/github/stars/HongshengHu/membership-inference-machine-learning-literature?style=social)\n* [Learn Prompting, Prompt Hacking](https://learnprompting.org/docs/category/-prompt-hacking)\n  * [MiesnerJacob / learn-prompting, Prompt Hacking](https://github.com/MiesnerJacob/learn-prompting/blob/main/08.%F0%9F%94%93%20Prompt%20Hacking.ipynb)![](https://img.shields.io/github/stars/MiesnerJacob/learn-prompting?style=social)\n* [Lakera AI's Gandalf](https://gandalf.lakera.ai/)\n* [leondz / garak](https://github.com/leondz/garak)![](https://img.shields.io/github/stars/leondz/garak?style=social)\n* [Microsoft AI Red Team building future of safer AI](https://www.microsoft.com/en-us/security/blog/2023/08/07/microsoft-ai-red-team-building-future-of-safer-ai/)\n* [OpenAI Red Teaming Network](https://openai.com/blog/red-teaming-network)\n* [r/ChatGPTJailbreak](https://www.reddit.com/r/ChatGPTJailbreak/)\n  * [developer mode fixed](https://www.reddit.com/r/ChatGPTJailbreak/comments/144905t/developer_mode_fixed/)\n* [A Safe Harbor for AI Evaluation and Red Teaming](https://arxiv.org/pdf/2403.04893)\n* [Y Combinator, ChatGPT Grandma Exploit](https://news.ycombinator.com/item?id=35630801)\n\n#### Generative AI Explainability\n\n* [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](http://sameersingh.org/files/papers/allennlp-interpret-demo-emnlp19.pdf)\n* [Attention Is All You Need](https://arxiv.org/pdf/1706.03762)\n* [Backpack Language Models](https://arxiv.org/pdf/2305.16765)\n* [Jay Alammar, Finding the Words to Say: Hidden State Visualizations for Language Models](https://jalammar.github.io/hidden-states/)\n* [Jay Alammar, Interfaces for Explaining Transformer Language Models](https://jalammar.github.io/explaining-transformers/)\n* [Neuronpedia](https://www.neuronpedia.org/)\n* [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html)\n* [Towards Monosemanticity: Decomposing Language Models With Dictionary Learning](https://transformer-circuits.pub/2023/monosemantic-features/index.html)\n* [Tracing the thoughts of a large language model](https://www.anthropic.com/research/tracing-thoughts-language-model)\n\n#### University Policies and Guidance\n\n* [Columbia Business School, Generative AI Policy](https://students.business.columbia.edu/office-of-student-affairs/academic-advising-and-student-success/academic-integrity/generative-ai-policy)\n* [Columbia University, Considerations for AI Tools in the Classroom](https://ctl.columbia.edu/resources-and-technology/resources/ai-tools/)\n* [Columbia University, Generative AI Policy](https://provost.columbia.edu/content/office-senior-vice-provost/ai-policy)\n* [Georgetown University, Artificial Intelligence and Homework Support Policies](https://cndls.georgetown.edu/resources/syllabus-policies/ai-and-homework-support/)\n* [Georgetown University, Artificial Intelligence (Generative) Resources](https://guides.library.georgetown.edu/ai)\n* [Georgetown University, Teaching with AI](https://cndls.georgetown.edu/resources/ai/)\n* [George Washington University, Faculty Resources: Generative AI](https://guides.himmelfarb.gwu.edu/faculty/generative-AI)\n* [George Washington University, Guidelines for Using Generative Artificial Intelligence at the George Washington University April 2023](https://provost.gwu.edu/sites/g/files/zaxdzs5926/files/2023-04/generative-artificial-intelligence-guidelines-april-2023.pdf)\n* [George Washington University, Guidelines for Using Generative Artificial Intelligence in Connection with Academic Work](https://provost.gwu.edu/guidelines-using-generative-artificial-intelligence-connection-academic-work-0)\n* [Harvard Business School, 2.1.2 Using ChatGPT & Artificial Intelligence (AI) Tools](https://www.hbs.edu/mba/handbook/standards-of-conduct/academic/Pages/chatgpt-and-ai.aspx)\n* [Harvard Graduate School of Education, HGSE AI Policy](https://registrar.gse.harvard.edu/AI-policy)\n* [Harvard University, AI Guidance & FAQs](https://oue.fas.harvard.edu/ai-guidance)\n* [Harvard University, Guidelines for Using ChatGPT and other Generative AI tools at Harvard](https://provost.harvard.edu/guidelines-using-chatgpt-and-other-generative-ai-tools-harvard)\n* [Massachusetts Institute of Technology, Initial guidance for use of Generative AI tools](https://ist.mit.edu/ai-guidance)\n* [Massachusetts Institute of Technology, Generative AI & Your Course](https://tll.mit.edu/teaching-resources/course-design/gen-ai-your-course/)\n* [Stanford Graduate School of Business, Course Policies on Generative AI Use](https://tlhub.stanford.edu/docs/course-policies-on-generative-ai-use/)\n* [Stanford University, Artificial Intelligence Teaching Guide](https://teachingcommons.stanford.edu/teaching-guides/artificial-intelligence-teaching-guide)\n* [Stanford University, Creating your course policy on AI](https://teachingcommons.stanford.edu/teaching-guides/artificial-intelligence-teaching-guide/creating-your-course-policy-ai)\n* [Stanford University, Generative AI Policy Guidance](https://communitystandards.stanford.edu/generative-ai-policy-guidance)\n* [Stanford University, Responsible AI at Stanford](https://uit.stanford.edu/security/responsibleai)\n* [University of California, AI Governance and Transparency](https://ai.universityofcalifornia.edu/governance-transparency/)\n* [University of California, Applicable Law and UC Policy](https://ai.universityofcalifornia.edu/governance-transparency/applicable-law-and-policy.html)\n* [University of California, Legal Alert: Artificial Intelligence Tools](https://www.ucop.edu/ethics-compliance-audit-services/_files/compliance/ai/ai-alert.pdf)\n* [University of California, Berkeley, AI at UC Berkeley](https://technology.berkeley.edu/AI)\n* [University of California, Berkeley, Appropriate Use of Generative AI Tools](https://ethics.berkeley.edu/privacy/appropriate-use-generative-ai-tools)\n* [University of California, Irvine, Generative AI for Teaching and Learning](https://dtei.uci.edu/generative-ai/)\n* [University of California, Irvine, Statement on Generative AI Detection](https://aisc.uci.edu/resources/Statement%20on%20Turnitin%20AI%20detection.pdf)\n* [University of California, Los Angeles, Artificial Intelligence (A.I.) Tools and Academic Use](https://guides.library.ucla.edu/c.php?g=1308287&p=9702196)\n* [University of California, Los Angeles, ChatGPT and AI Resources](https://online.ucla.edu/chatgpt-and-ai-resources/)\n* [University of California, Los Angeles, Generative AI](https://genai.ucla.edu/)\n* [University of California, Los Angeles, Guiding Principles for Responsible Use](https://genai.ucla.edu/guiding-principles-responsible-use)\n* [University of California, Los Angeles, Teaching Guidance for ChatGPT and Related AI Developments](https://senate.ucla.edu/news/teaching-guidance-chatgpt-and-related-ai-developments)\n* [University of Notre Dame, AI Recommendations for Instructors](https://honorcode.nd.edu/ai-recommendations-for-instructors/)\n* [University of Notre Dame, AI@ND Policies and Guidelines](https://ai.nd.edu/policies-and-guidelines/)\n* [University of Notre Dame, Generative AI Policy for Students](https://honorcode.nd.edu/generative-ai-policy-for-students-august-2023/)\n* [University of Southern California, Using Generative AI in Research](https://libguides.usc.edu/generative-AI/home)\n* [University of Washington, AI+Teaching](https://teaching.washington.edu/course-design/ai/)\n* [University of Washington, AI+Teaching, Sample syllabus statements regarding student use of artificial intelligence](https://teaching.washington.edu/course-design/ai/sample-ai-syllabus-statements/)\n* [Yale University, AI at Yale](https://ai.yale.edu/)\n* [Yale University, AI Guidance for Teachers](https://poorvucenter.yale.edu/AIguidance)\n* [Yale University, Yale University AI guidelines for staff](https://yaledata.yale.edu/yale-university-ai-guidelines-staff)\n* [Yale University, Guidelines for the Use of Generative AI Tools](https://provost.yale.edu/news/guidelines-use-generative-ai-tools)\n\n### Conferences and Workshops\n\nThis section is for conferences, workshops and other major events related to responsible ML.\n\n* [AAAI Conference on Artificial Intelligence](https://aaai.org/conference/aaai/)\n* [ACM FAccT (Fairness, Accountability, and Transparency)](https://facctconference.org/)\n  * [FAT/ML (Fairness, Accountability, and Transparency in Machine Learning)](https://www.fatml.org/) \n* [ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO)](https://eaamo.org/)\n* [AIES (AAAI/ACM Conference on AI, Ethics, and Society)](https://www.aies-conference.com/) \n* [Black in AI](https://blackinai.github.io/#/)\n* [Computer Vision and Pattern Recognition (CVPR)](https://thecvf.com/)\n* [Evaluating Generative AI Systems: the Good, the Bad, and the Hype (April 15, 2024)](https://dc-workshop.genlaw.org/)\n* [IAPP, AI Governance Global 2024, June 4-7, 2024](https://iapp.org/conference/iapp-ai-governance-global/)\n* [International Conference on Machine Learning (ICML)](https://icml.cc/)\n  * **2020**:\n    * [2nd ICML Workshop on Human in the Loop Learning (HILL)](https://icml.cc/virtual/2020/workshop/5747)\n    * [5th ICML Workshop on Human Interpretability in Machine Learning (WHI)](https://icml.cc/virtual/2020/workshop/5746)\n    * [Challenges in Deploying and Monitoring Machine Learning Systems](https://icml.cc/virtual/2020/workshop/5738)\n    * [Economics of privacy and data labor](https://icml.cc/virtual/2020/workshop/5723)\n    * [Federated Learning for User Privacy and Data Confidentiality](https://icml.cc/virtual/2020/workshop/5730)\n    * [Healthcare Systems, Population Health, and the Role of Health-tech](https://icml.cc/virtual/2020/workshop/5726)\n    * [Law & Machine Learning](https://icml.cc/virtual/2020/workshop/5718)\n    * [ML Interpretability for Scientific Discovery](https://icml.cc/virtual/2020/workshop/5740)\n    * [MLRetrospectives: A Venue for Self-Reflection in ML Research](https://icml.cc/virtual/2020/workshop/5739)\n    * [Participatory Approaches to Machine Learning](https://icml.cc/virtual/2020/workshop/5720)\n    * [XXAI: Extending Explainable AI Beyond Deep Models and Classifiers](https://icml.cc/virtual/2020/workshop/5734)\n  * **2021**:\n    * [Human-AI Collaboration in Sequential Decision-Making](https://icml.cc/virtual/2021/workshop/8367)\n    * [Machine Learning for Data: Automated Creation, Privacy, Bias](https://icml.cc/virtual/2021/workshop/8356)\n    * [ICML Workshop on Algorithmic Recourse](https://icml.cc/virtual/2021/workshop/8363)\n    * [ICML Workshop on Human in the Loop Learning (HILL)](https://icml.cc/virtual/2021/workshop/8362)\n    * [ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI](https://icml.cc/virtual/2021/workshop/8355)\n    * [Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3)](https://icml.cc/virtual/2021/workshop/8365)\n    * [International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21)](https://icml.cc/virtual/2021/workshop/8359)\n    * [Interpretable Machine Learning in Healthcare](https://icml.cc/virtual/2021/workshop/8358)\n    * [Self-Supervised Learning for Reasoning and Perception](https://icml.cc/virtual/2021/workshop/8353)\n    * [The Neglected Assumptions In Causal Inference](https://icml.cc/virtual/2021/workshop/8349)\n    * [Theory and Practice of Differential Privacy](https://icml.cc/virtual/2021/workshop/8376)\n    * [Uncertainty and Robustness in Deep Learning](https://icml.cc/virtual/2021/workshop/8374)\n    * [Workshop on Computational Approaches to Mental Health @ ICML 2021](https://icml.cc/virtual/2021/workshop/8352)\n    * [Workshop on Distribution-Free Uncertainty Quantification](https://icml.cc/virtual/2021/workshop/8373)\n    * [Workshop on Socially Responsible Machine Learning](https://icml.cc/virtual/2021/workshop/8347)\n  * **2022**:\n    * [1st ICML 2022 Workshop on Safe Learning for Autonomous Driving (SL4AD)](https://icml.cc/virtual/2022/workshop/13475)\n    * [2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH)](https://icml.cc/virtual/2022/workshop/13449)\n    * [DataPerf: Benchmarking Data for Data-Centric AI](https://icml.cc/virtual/2022/workshop/13477)\n    * [Disinformation Countermeasures and Machine Learning (DisCoML)](https://icml.cc/virtual/2022/workshop/13446)\n    * [Responsible Decision Making in Dynamic Environments](https://icml.cc/virtual/2022/workshop/13453)\n    * [Spurious correlations, Invariance, and Stability (SCIS)](https://icml.cc/virtual/2022/workshop/13461)\n    * [The 1st Workshop on Healthcare AI and COVID-19](https://icml.cc/virtual/2022/workshop/13469)\n    * [Theory and Practice of Differential Privacy](https://icml.cc/virtual/2022/workshop/13448)\n    * [Workshop on Human-Machine Collaboration and Teaming](https://icml.cc/virtual/2022/workshop/13478)\n  * **2023**:\n    * [2nd ICML Workshop on New Frontiers in Adversarial Machine Learning](https://icml.cc/virtual/2023/workshop/21487)\n    * [2nd Workshop on Formal Verification of Machine Learning](https://icml.cc/virtual/2023/workshop/21471)\n    * [3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)](https://icml.cc/virtual/2023/workshop/21486)\n    * [Challenges in Deployable Generative AI](https://icml.cc/virtual/2023/workshop/21481)\n    * [“Could it have been different?” Counterfactuals in Minds and Machines](https://icml.cc/virtual/2023/workshop/21482)\n    * [Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities](https://icml.cc/virtual/2023/workshop/21473)\n    * [Generative AI and Law (GenLaw)](https://icml.cc/virtual/2023/workshop/21490)\n    * [Interactive Learning with Implicit Human Feedback](https://icml.cc/virtual/2023/workshop/21477)\n    * [Neural Conversational AI Workshop - What’s left to TEACH (Trustworthy, Enhanced, Adaptable, Capable and Human-centric) chatbots?](https://icml.cc/virtual/2023/workshop/21485)\n    * [The Second Workshop on Spurious Correlations, Invariance and Stability](https://icml.cc/virtual/2023/workshop/21493)\n* [Knowledge, Discovery, and Data Mining (KDD)](https://kdd.org/)\n  * **2023**:\n    * [2nd ACM SIGKDD Workshop on Ethical Artificial Intelligence: Methods and Applications](https://charliezhaoyinpeng.github.io/EAI-KDD23/cfp/)\n    * [KDD Data Science for Social Good 2023](https://kdd-dssg.github.io/)\n* [Mission Control AI, Booz Allen Hamilton, and The Intellectual Forum at Jesus College, Cambridge, The 2024 Leaders in Responsible AI Summit, March 22, 2024](https://usemissioncontrol.com/summit/)\n* [NAACL 24 Tutorial: Explanations in the Era of Large Language Models](https://explanation-llm.github.io/)\n* [Neural Information Processing Systems (NeurIPs)](https://neurips.cc/)\n  * **2022**:\n    * [5th Robot Learning Workshop: Trustworthy Robotics](https://neurips.cc/virtual/2022/workshop/49997)\n    * [Algorithmic Fairness through the Lens of Causality and Privacy](https://neurips.cc/virtual/2022/workshop/49967)\n    * [Causal Machine Learning for Real-World Impact](https://neurips.cc/virtual/2022/workshop/49993)\n    * [Challenges in Deploying and Monitoring Machine Learning Systems](https://neurips.cc/virtual/2022/workshop/49982)\n    * [Cultures of AI and AI for Culture](https://neurips.cc/virtual/2022/workshop/49983)\n    * [Empowering Communities: A Participatory Approach to AI for Mental Health](https://neurips.cc/virtual/2022/workshop/49984)\n    * [Federated Learning: Recent Advances and New Challenges](https://neurips.cc/virtual/2022/workshop/50002)\n    * [Gaze meets ML](https://neurips.cc/virtual/2022/workshop/49990)\n    * [HCAI@NeurIPS 2022, Human Centered AI](https://neurips.cc/virtual/2022/workshop/50008)\n    * [Human Evaluation of Generative Models](https://neurips.cc/virtual/2022/workshop/49978)\n    * [Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022](https://neurips.cc/virtual/2022/workshop/49957)\n    * [I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification](https://neurips.cc/virtual/2022/workshop/49960)\n    * [Learning Meaningful Representations of Life](https://neurips.cc/virtual/2022/workshop/49966)\n    * [Machine Learning for Autonomous Driving](https://neurips.cc/virtual/2022/workshop/49981)\n    * [Progress and Challenges in Building Trustworthy Embodied AI](https://neurips.cc/virtual/2022/workshop/49972)\n    * [Tackling Climate Change with Machine Learning](https://neurips.cc/virtual/2022/workshop/49964)\n    * [Trustworthy and Socially Responsible Machine Learning](https://neurips.cc/virtual/2022/workshop/49959)\n    * [Workshop on Machine Learning Safety](https://neurips.cc/virtual/2022/workshop/49986)\n  * **2023**: \n    * [AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics](https://neurips.cc/virtual/2023/workshop/66528)\n    * [Algorithmic Fairness through the Lens of Time](https://neurips.cc/virtual/2023/workshop/66502)\n    * [Attributing Model Behavior at Scale (ATTRIB)](https://neurips.cc/virtual/2023/workshop/66496)\n    * [Backdoors in Deep Learning: The Good, the Bad, and the Ugly](https://neurips.cc/virtual/2023/workshop/66550)\n    * [Computational Sustainability: Promises and Pitfalls from Theory to Deployment](https://neurips.cc/virtual/2023/workshop/66523)\n    * [I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models](https://neurips.cc/virtual/2023/workshop/66506)\n    * [Socially Responsible Language Modelling Research (SoLaR)](https://neurips.cc/virtual/2023/workshop/66526)\n    * [Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations](https://neurips.cc/virtual/2023/workshop/66512)\n    * [Workshop on Distribution Shifts: New Frontiers with Foundation Models](https://neurips.cc/virtual/2023/workshop/66509)\n    * [XAI in Action: Past, Present, and Future Applications](https://neurips.cc/virtual/2023/workshop/66529)\n * [OECD.AI, Building the foundations for collaboration: The OECD-African Union AI Dialogue](https://oecd.ai/en/wonk/oecd-au-ai-dialogue)\n * [Oxford Generative AI Summit Slides](https://drive.google.com/drive/folders/1WQPaL-ozhZbZaDichFm4gWZQpGwriT32)\n\n### Official Policy, Frameworks, and Guidance\n\nThis section serves as a repository for policy documents, regulations, guidelines, and recommendations that govern the ethical and responsible use of artificial intelligence and machine learning technologies. From international legal frameworks to specific national laws, the resources cover a broad spectrum of topics such as fairness, privacy, ethics, and governance. \n\n#### Australia\n* [Department of Industry, Science and Resources, AI Governance: Leadership insights and the Voluntary AI Safety Standard in practice](https://www.industry.gov.au/news/ai-governance-leadership-insights-and-voluntary-ai-safety-standard-practice)\n* [Department of Industry, Science and Resources, The AI Impact Navigator, October 21, 2024](https://www.industry.gov.au/publications/ai-impact-navigator)\n* [Department of Industry, Science and Resources, Australia’s AI Ethics Principles](https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles)\n* [Department of Industry, Science and Resources, Introducing mandatory guardrails for AI in high-risk settings: proposals paper](https://consult.industry.gov.au/ai-mandatory-guardrails)\n* [Department of Industry, Sciences and Resources, Voluntary AI Safety Standard, August 2024](https://www.industry.gov.au/sites/default/files/2024-09/voluntary-ai-safety-standard.pdf)\n* [Digital Transformation Agency, Evaluation of the whole-of-government trial of Microsoft 365 Copilot: Summary of evaluation findings, October 23, 2024](https://www.digital.gov.au/sites/default/files/documents/2024-10/Copilot%20Microsoft%20365%20summary%20of%20evaluation%20findings.pdf)\n* [Digital Transformation Agency, Policy for the responsible use of AI in government, September 2024, Version 1.1](https://www.digital.gov.au/sites/default/files/documents/2024-08/Policy%20for%20the%20responsible%20use%20of%20AI%20in%20government%20v1.1.pdf)\n* [Office of the Australian Information Commissioner, Guidance on privacy and developing and training generative AI models](https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-developing-and-training-generative-ai-models)\n* [Office of the Australian Information Commissioner, Guidance on privacy and the use of commercially available AI products](https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products)\n* [National framework for the assurance of artificial intelligence in government](https://www.finance.gov.au/sites/default/files/2024-06/National-framework-for-the-assurance-of-AI-in-government.pdf)\n* [Testing the Reliability, Validity, and Equity of Terrorism Risk Assessment Instruments](https://www.homeaffairs.gov.au/foi/files/2023/fa-230400097-document-released-part-1.PDF)\n\n#### Brazil\n* [Autoridade Nacional de Proteção de Dados (ANPD) (Brazilian Data Protection Authority), Technology Radar – short version in English, no. 1: Generative Artificial Intelligence](https://www.gov.br/anpd/pt-br/documentos-e-publicacoes/documentos-de-publicacoes/radar-tecnologico-inteligencia-artificial-generativa-versao-em-lingua-inglesa.pdf)\n\n#### Canada\n* [An Act to enact the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act and the Artificial Intelligence and Data Act and to make consequential and related amendments to other Acts](https://www.parl.ca/legisinfo/en/bill/44-1/c-27)\n* [Algorithmic Impact Assessment tool](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html)\n* [A Regulatory Framework for AI: Recommendations for PIPEDA Reform](https://www.priv.gc.ca/en/about-the-opc/what-we-do/consultations/completed-consultations/consultation-ai/reg-fw_202011/)\n* [Artificial Intelligence and Data Act](https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act)\n* [The Artificial Intelligence and Data Act (AIDA) – Companion document](https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document)\n* [Directive on Automated Decision Making (Canada)](https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592)\n* [(Draft Guideline) E-23 – Model Risk Management](https://www.osfi-bsif.gc.ca/en/guidance/guidance-library/draft-guideline-e-23-model-risk-management)\n* [Health Canada, Transparency for machine learning-enabled medical devices: Guiding principles](https://www.canada.ca/en/health-canada/services/drugs-health-products/medical-devices/transparency-machine-learning-guiding-principles.html)\n\n#### Colombia\n* [Presidency of the Republic of Colombia, Marco Ético para la Inteligencia Artificial en Colombia (Ethical Framework for Artificial Intelligence in Colombia), November 2021](https://minciencias.gov.co/sites/default/files/marco-etico-ia-colombia-2021.pdf)\n\n#### Costa Rica\n* [Ministerio de Ciencia, Innovación, Tecnología y Telecomunicaciones (MICITT), Plan Nacional de Ciencia, Tecnología e Innovación 2022–2027](https://cambioclimatico.go.cr/wp-content/uploads/2023/06/Plan-Nacional-Ciencia-Tecnologia-Innovacion-2022-2027.pdf)\n\n#### Denmark\n* Ministry of Finance and Ministry of Industry, Business and Financial Affairs, March 2019 | [National Strategy for Artificial Intelligence](https://en.digst.dk/media/lz0fxbt4/305755_gb_version_final-a.pdf)\n\n#### Finland\n* [Ministry of Economic Affairs and Employment, Finland's Age of Artificial Intelligence: Turning Finland into a leading country in the application of artificial intelligence. Objective and recommendations for measures](https://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/160391/TEMrap_47_2017_verkkojulkaisu.pdf)\n\n#### France\n* [Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (France)](https://acpr.banque-france.fr/sites/default/files/medias/documents/20200612_gouvernance_evaluation_ia.pdf)\n* VIGINUM | February 7, 2025 | [Challenges and opportunities of artificial intelligence in the fight against information manipulation](https://www.sgdsn.gouv.fr/files/files/Publications/20250207_NP_SGDSN_VIGINUM_Rapport%20menace%20informationnelle%20IA_EN_0.pdf)\n\n#### Germany\n* [Bundesamt für Sicherheit in der Informationstechnik, Generative AI Models - Opportunities and Risks for Industry and Authorities](https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/KI/Generative_AI_Models.html)\n* [Bundesamt für Sicherheit in der Informationstechnik, German-French recommendations for the use of AI programming assistants](https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/KI/ANSSI_BSI_AI_Coding_Assistants.html)\n* [Daten Ethik Kommission, Recommendations of the Data Ethics Commission for the Federal Government's Strategy on Artificial Intelligence, October 9, 2018](https://www.bmi.bund.de/SharedDocs/downloads/EN/themen/it-digital-policy/recommendations-data-ethics-commission.pdf?__blob=publicationFile&v=3)\n* [Germany AI Strategy Report](https://ai-watch.ec.europa.eu/countries/germany/germany-ai-strategy-report_en)\n* [OECD-Bericht zu Künstlicher Intelligenz in Deutschland](https://www.ki-strategie-deutschland.de/files/downloads/OECD-Bericht_K%C3%BCnstlicher_Intelligenz_in_Deutschland.pdf)\n\n#### Hong Kong\n* [Office of the Privacy Commissioner for Personal Data, Artificial Intelligence: Model Personal Data Protection Framework, June 2024](https://www.pcpd.org.hk/english/resources_centre/publications/files/ai_protection_framework.pdf)\n\n#### Iceland\n* [Ministry of Higher Education, Industry, and Innovation | Aðgerðaáætlun um gervigreind 2024-2026 (Action Plan for Artificial Intelligence 2024-2026) | November 2024](https://samradapi.island.is/api/Documents/1d4c7cba-fd9c-ef11-9bc7-005056bcce7e)\n* [Ministry of Higher Education, Industry, and Innovation | Statistics Iceland | Efnahagsleg tækifæri gervigreindar á Íslandi (Economic Opportunities of Artificial Intelligence in Iceland)](https://samradapi.island.is/api/Documents/1e4c7cba-fd9c-ef11-9bc7-005056bcce7e)\n\n#### Ireland\n* [AI - Here for Good: A National Artificial Intelligence Strategy for Ireland](https://enterprise.gov.ie/en/publications/publication-files/national-ai-strategy.pdf)\n* [Department of Finance and Department of Enterprise, Trade and Employment, Artificial Intelligence: Friend or Foe? Summary and Public Policy Considerations, June 2024](https://www.gov.ie/pdf/?file=https://assets.gov.ie/295620/f11c6c66-4012-49fa-bb7f-8f14130f6fa5.pdf)\n* [Department of Public Expenditure, NDP Delivery and Reform, Interim Guidelines for Use of AI in the Public Service, February 2024](https://assets.gov.ie/280459/73ce75af-0015-46af-a9f6-b54f0a3c4fd0.pdf)\n* [Ireland's National AI Strategy: AI - Here for Good, Refresh 2024](https://enterprise.gov.ie/en/publications/publication-files/national-ai-strategy-refresh-2024.pdf)\n* [National Standards Authority of Ireland, Top Team on Standards in AI, AI Standards & Assurance Roadmap: Action under 'AI - Here for Good,' the National Artificial Intelligence Strategy for Ireland, July 2023](https://www.nsai.ie/images/uploads/general/NSAI_AI_report_digital_links.pdf)\n\n#### Jamaica\n* National Artificial Intelligence Task Force, September 2024 | [National Artificial Intelligence Policy Recommendations](https://opm.gov.jm/wp-content/uploads/2025/02/National-Artificial-Intelligence-Task-Force-Policy-Recommendations-Final-1.pdf)\n\n#### Japan\n* [Japan AI Safety Institute, Guide to Evaluation Perspectives on AI Safety (Version 1.01), September 25, 2024](https://aisi.go.jp/assets/pdf/ai_safety_eval_v1.01_en.pdf)\n* [Japan AI Safety Institute, Guide to Red Teaming Methodology on AI Safety (Version 1.00), September 25, 2024](https://aisi.go.jp/assets/pdf/ai_safety_RT_v1.00_en.pdf)\n\n#### Malaysia\n* [The National Guidelines on AI Governance & Ethics](https://mastic.mosti.gov.my/publication/the-national-guidelines-on-ai-governance-ethics/)\n\n#### Mexico\n* [Instituto Nacional de Transparencia, Acceso a la Información y Protección de Datos Personales (INAI), Recomendaciones para el Tratamiento de Datos Personales Derivado del Uso de la Inteligencia Artificial, June 2024](https://home.inai.org.mx/wp-content/documentos/DocumentosSectorPublico/RecomendacionesPDP-IA.pdf)\n\n#### Moldova\n* Ministry of Economic Development and Digitalization, 2024 | [Cartea Albă cu Privire la Inteligența Artificială și Guvernanța Datelor](https://drive.google.com/file/d/1MDEGQ3snOiYXeM5G1YZfV8yH6ZFWxVTJ/view)\n  * Ministry of Economic Development and Digitalization, 2024 | [White Book on Artificial Intelligence and Data Governance](https://drive.google.com/file/d/1d2VmubZJjwVjzxUT4gjJE7DXTinzdyfO/view?usp=sharing)\n\n#### Netherlands\n* [Autoriteit Persoonsgegevens, Call for input on prohibition on AI systems for emotion recognition in the areas of workplace or education institutions (October 31, 2024)](https://www.autoriteitpersoonsgegevens.nl/en/documents/call-for-input-on-prohibition-on-ai-systems-for-emotion-recognition-in-the-areas-of-workplace-or-education-institutions)\n* [Autoriteit Persoonsgegevens, scraping bijna altijd illegal (Dutch Data Protection Authority, \"Scraping is always illegal\")](https://www.autoriteitpersoonsgegevens.nl/actueel/ap-scraping-bijna-altijd-illegaal)\n* [General principles for the use of Artificial Intelligence in the financial sector](https://www.dnb.nl/media/jkbip2jc/general-principles-for-the-use-of-artificial-intelligence-in-the-financial-sector.pdf)\n* [Ministry of Infrastructure and Water Management | AI Impact Assessment: The tool for a responsible AI project](https://www.government.nl/binaries/government/documenten/publications/2023/03/02/ai-impact-assessment/2024-IWM-AI-Impact-assessment-2.0-EN.pdf)\n\n#### New Zealand\n* [AI Safety Institute (AISI), Advanced AI evaluations at AISI: May update](https://www.aisi.gov.uk/work/advanced-ai-evaluations-may-update)\n* [Algorithm Charter for Aotearoa New Zealand](https://data.govt.nz/assets/data-ethics/algorithm/Algorithm-Charter-2020_Final-English-1.pdf)\n* [Callaghan Innovation, EU AI Fact Sheet 4, High-risk AI systems](https://www.callaghaninnovation.govt.nz/assets/documents/Resource-EU-AI-Act-Support/EU-AI-Policy-Fact-Sheet-4-High-Risk-AI-Systems.pdf)\n* [Initial advice on Generative Artificial Intelligence in the public service | July 2023](https://www.digital.govt.nz/assets/Standards-guidance/Technology-and-architecture/Generative-AI/Joint-System-Leads-tactical-guidance-on-public-service-use-of-GenAI-September-2023.pdf)\n\n#### Norway\n* Ministry of Local Government and Modernisation | [National Strategy for Artificial Intelligence](https://www.regjeringen.no/contentassets/1febbbb2c4fd4b7d92c67ddd353b6ae8/en-gb/pdfs/ki-strategi_en.pdf)\n* Ministry of Local Government and Regional Development, Expert Group on Artificial Intelligence and Elections, February 2025 | [Artificial Intelligence and Democratic Elections — International Experiences and National Recommendations](https://www.regjeringen.no/contentassets/23f8fd1726634f589724d96b49fe994c/en_rapport-ekspertgruppe-ki-og-valg.pdf)\n\n#### Philippines\n\n#### Singapore\n* [Monetary Authority of Singapore, Artificial Intelligence Model Risk Management: Observations from a Thematic Review (Information Paper, December 2024](https://www.mas.gov.sg/-/media/mas-media-library/publications/monographs-or-information-paper/imd/2024/information-paper-on-ai-risk-management-final.pdf)\n* [Personal Data Protection Commission (PDPC), Companion to the Model AI Governance Framework – Implementation and Self-Assessment Guide for Organizations](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/resource-for-organisation/ai/sgisago.pdf)\n* [Personal Data Protection Commission (PDPC), Compendium of Use Cases: Practical Illustrations of the Model AI Governance Framework](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/resource-for-organisation/ai/sgaigovusecases.pdf)\n* [Personal Data Protection Commission (PDPC), Model Artificial Intelligence Governance Framework (Second Edition)](https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/SGModelAIGovFramework2.pdf)\n* [Personal Data Protection Commission (PDPC), Privacy Enhancing Technology (PET): Proposed Guide on Synthetic Data Generation](https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/other-guides/proposed-guide-on-synthetic-data-generation.pdf)\n\n#### South Korea\n* [National Assembly, 인공지능 발전과 신뢰 기반 조성 등에 관한 기본법안(대안), Basic Act on the Promotion of Artificial Intelligence Development and Establishment of a Trust Framework (Alternative Draft)](https://likms.assembly.go.kr/bill/billDetail.do?billId=PRC_R2V4H1W1T2K5M1O6E4Q9T0V7Q9S0U0)\n\n#### Switzerland\n* [Digital Switzerland Strategy 2025](https://digital.swiss/userdata/uploads/strategie-dch-en.pdf)\n\n#### Ukraine\n* Ministry of Digital Transformation | [White Paper on Artificial Intelligence Regulation in Ukraine: Vision of the Ministry of Digital Transformation of Ukraine, Version for Consultation](https://thedigital.gov.ua/storage/uploads/files/page/community/docs/%D0%91%D1%96%D0%BB%D0%B0_%D0%BA%D0%BD%D0%B8%D0%B3%D0%B0_%D0%B7_%D1%80%D0%B5%D0%B3%D1%83%D0%BB%D1%8E%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D0%A8%D0%86_%D0%B2_%D0%A3%D0%BA%D1%80%D0%B0%D1%97%D0%BD%D1%96_%D0%90%D0%9D%D0%93%D0%9B.pdf)\n* Ministry of Digital Transformation | [Дорожня карта з регулювання штучного інтелекту в Україні: Bottom-Up Підхід](https://cms.thedigital.gov.ua/storage/uploads/files/page/community/docs/%D0%94%D0%BE%D1%80%D0%BE%D0%B6%D0%BD%D1%8F_%D0%BA%D0%B0%D1%80%D1%82%D0%B0_%D0%B7_%D1%80%D0%B5%D0%B3%D1%83%D0%BB%D1%8E%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D0%A8%D0%86_%D0%B2_%D0%A3%D0%BA%D1%80%D0%B0%D1%97%D0%BD%D1%96_compressed.pdf)\n* Ministry of Digital Transformation, Ministry of Culture and Information Policy, and National Council of Television and Radio Broadcasting | [Guidelines on the Responsible Use of Artificial Intelligence in the News Media](https://webportal.nrada.gov.ua/wp-content/uploads/2024/05/Ukraine-AI-Guidelines-for-Media.pdf)\n\n#### United Kingdom\n* [AI Safety Institute (AISI), Safety cases at AISI](https://www.aisi.gov.uk/work/safety-cases-at-aisi)\n* [Department for Science, Innovation and Technology and AI Safety Institute | International Scientific Report on the Safety of Advanced AI](https://www.gov.uk/government/publications/international-scientific-report-on-the-safety-of-advanced-ai)\n* [Department for Science, Innovation and Technology | The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023](https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration/the-bletchley-declaration-by-countries-attending-the-ai-safety-summit-1-2-november-2023)\n* [Department for Science, Innovation and Technology | Evaluation of the Cyber AI Hub programme | January 8, 2025](https://www.gov.uk/government/publications/evaluation-of-the-northern-ireland-cyber-ai-hub-programme/evaluation-of-the-cyber-ai-hub-programme)\n* [Department for Science, Innovation and Technology | Frontier AI: capabilities and risks](https://www.gov.uk/government/publications/frontier-ai-capabilities-and-risks-discussion-paper)\n* [Department for Science, Innovation and Technology | Global Coalition on Telecommunications: principles on AI adoption in the telecommunications industry | January 16, 2025](https://www.gov.uk/government/publications/global-coalition-on-telecommunications-principles-on-ai-adoption-in-the-telecommunications-industry/global-coalition-on-telecommunications-principles-on-ai-adoption-in-the-telecommunications-industry)\n* [Department for Science, Innovation and Technology, Guidance | Introduction to AI Assurance](https://www.gov.uk/government/publications/introduction-to-ai-assurance)\n* Government Digital Service and Department for Science, Innovation & Technology, February 2025 | [Artificial Intelligence Playbook for the UK Government](https://assets.publishing.service.gov.uk/media/67aca2f7e400ae62338324bd/AI_Playbook_for_the_UK_Government__12_02_.pdf)\n* [Information Commissioner's Office (ICO) | AI tools in recruitment | November 6, 2024](https://ico.org.uk/action-weve-taken/audits-and-overview-reports/ai-tools-in-recruitment/)\n* [National Physical Laboratory (NPL) | Beginner's guide to measurement GPG118](https://www.npl.co.uk/gpgs/beginners-guide-to-measurement)\n* [Northern Ireland response to the AI Council AI Roadmap](https://matrixni.org/wp-content/uploads/2021/04/NI-Response-to-UK-AI-roadmap.pdf)\n* [Ofcom | Red Teaming for GenAI Harms: Revealing the Risks and Rewards for Online Safety | July 23, 2024](https://www.ofcom.org.uk/siteassets/resources/documents/consultations/discussion-papers/red-teaming/red-teaming-for-gen-ai-harms.pdf?v=370762)\n* [Online Harms White Paper: Full government response to the consultation](https://www.gov.uk/government/consultations/online-harms-white-paper)\n* [Parliamentary Office of Science and Technology (POST) | POSTnote 735, Energy Security and AI](https://researchbriefings.files.parliament.uk/documents/POST-PN-0735/POST-PN-0735.pdf)\n* [The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018](https://www.legislation.gov.uk/uksi/2018/852/contents/made)\n* [US AISI and UK AISI Joint Pre-Deployment Test: Anthropic's Claude 3.5 Sonnet (October 2024 Release)](https://www.nist.gov/system/files/documents/2024/11/19/Upgraded%20Sonnet-Publication-US.pdf)\n* [US AISI and UK AISI Joint Pre-Deployment Test: OpenAI o1 (December 2024)](https://www.nist.gov/system/files/documents/2024/12/18/US_UK_AI%20Safety%20Institute_%20December_Publication-OpenAIo1.pdf)\n\n#### United States (Federal Government)\n\n**Consumer Financial Protection Bureau (CFPB)**  \n* [12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)](https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1002/)\n* [Chatbots in consumer finance](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/)\n* [Innovation spotlight: Providing adverse action notices when using AI/ML models](https://www.consumerfinance.gov/about-us/blog/innovation-spotlight-providing-adverse-action-notices-when-using-ai-ml-models/)\n\n**Commodity Futures Trading Commission (CFTC)**  \n* [A Primer on Artificial Intelligence in Securities Markets](https://www.cftc.gov/media/2846/LabCFTC_PrimerArtificialIntelligence102119/download)\n* [Responsible Artificial Intelligence in Financial Markets](https://www.cftc.gov/PressRoom/PressReleases/8905-24)\n\n**Congressional Budget Office**\n* [H.R. 9720, AI Incident Reporting and Security Enhancement Act](https://www.cbo.gov/system/files/2024-10/hr9720.pdf)\n\n**Congressional Research Service** \n* [Artificial Intelligence (AI) in Health Care, December 30, 2024](https://crsreports.congress.gov/product/pdf/R/R48319)\n* [Artificial Intelligence and Machine Learning in Financial Services, April 3, 2024](https://crsreports.congress.gov/product/pdf/R/R47997)\n* [Artificial Intelligence: Background, Selected Issues, and Policy Considerations, May 19, 2021](https://crsreports.congress.gov/product/pdf/R/R46795)\n* [Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th Congress, August 4, 2023](https://www.energy.gov/sites/default/files/2023-09/Artificial%20Intelligence%20Overview%2C%20Recent%20Advances%2C%20and%20Considerations%20for%20the%20118th%20Congress.pdf)\n* [Highlights of the 2023 Executive Order on Artificial Intelligence for Congress, November 17, 2023](https://crsreports.congress.gov/product/pdf/R/R47843/2)\n\n**Copyright Office**\n* [Copyright and Artificial Intelligence, Part 1: Digital Replicas, July 2024](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-1-Digital-Replicas-Report.pdf)\n\n**Data.gov**\n* [Privacy Policy and Data Policy](https://data.gov/privacy-policy/)\n \n**Defense Advanced Research Projects Agency (DARPA)**\n* [Explainable Artificial Intelligence (XAI) (Archived)](https://www.darpa.mil/program/explainable-artificial-intelligence)  \n\n**Defense Technical Information Center**  \n* [Computer Security Technology Planning Study, October 1, 1972](https://apps.dtic.mil/sti/citations/AD0758206)\n\n**Department of Agriculture (USDA)**\n* [Fiscal Year 2025-2026 AI Strategy](https://www.usda.gov/sites/default/files/documents/fy-2025-2026-usda-ai-strategy.pdf)\n  \n**Department of Commerce**\n* [Artificial intelligence](https://www.commerce.gov/issues/artificial-intelligence)\n* [Intellectual property](https://www.commerce.gov/issues/intellectual-property)\n* **[National Institute of Standards and Technology (NIST)](https://www.nist.gov/)**\n  * [AI 100-1 Artificial Intelligence Risk Management Framework (NIST AI RMF 1.0)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf)\n  * [De-identification Tools](https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/focus-areas/de-id/tools)\n  * [NIST AI 100-2 E2023: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations](https://csrc.nist.gov/pubs/ai/100/2/e2023/final)\n  * [Assessing Risks and Impacts of AI (ARIA)](https://ai-challenges.nist.gov/aria/library)\n  * [Four Principles of Explainable Artificial Intelligence, Draft NISTIR 8312, 2020-08-17](https://www.nist.gov/system/files/documents/2020/08/17/NIST%20Explainable%20AI%20Draft%20NISTIR8312%20%281%29.pdf)\n  * [Four Principles of Explainable Artificial Intelligence, NISTIR 8312, 2021-09-29](https://www.nist.gov/publications/four-principles-explainable-artificial-intelligence)\n  * [Engineering Statistics Handbook](https://doi.org/10.18434/M32189)\n  * [Measurement Uncertainty](https://www.nist.gov/itl/sed/topic-areas/measurement-uncertainty)\n    * [International Bureau of Weights and Measures (BIPM), Evaluation of measurement data—Guide to the expression of uncertainty in measurement](https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6)\n  * [NIST Special Publication 800-30 Revision 1, Guide for Conducting Risk Assessments](https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-30r1.pdf)\n  * [Psychological Foundations of Explainability and Interpretability in Artificial Intelligence](https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8367.pdf)\n* **[U.S. Artificial Intelligence Safety Institute (USAISI)](https://www.nist.gov/aisi)**\n  * [US AISI and UK AISI Joint Pre-Deployment Test: Anthropic's Claude 3.5 Sonnet (October 2024 Release)](https://www.nist.gov/system/files/documents/2024/11/19/Upgraded%20Sonnet-Publication-US.pdf)\n  * [US AISI and UK AISI Joint Pre-Deployment Test: OpenAI o1 (December 2024)](https://www.nist.gov/system/files/documents/2024/12/18/US_UK_AI%20Safety%20Institute_%20December_Publication-OpenAIo1.pdf)\n* **[National Telecommunications and Information Administration (NTIA)](https://www.ntia.gov/)**\n  * [AI Accountability Policy Report](https://www.ntia.gov/issues/artificial-intelligence/ai-accountability-policy-report)\n  * [AI System Documentation](https://www.ntia.gov/issues/artificial-intelligence/ai-accountability-policy-report/developing-accountability-inputs-a-deeper-dive/information-flow/ai-system-documentation)\n  * [Internet Policy Task Force, Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy Framework](https://www.ntia.doc.gov/files/ntia/publications/iptf_privacy_greenpaper_12162010.pdf)\n  * [NTIA Artificial Intelligence Accountability Policy Report, March 2024](https://www.ntia.gov/sites/default/files/publications/ntia-ai-report-final.pdf)\n\n**Department of Defense**  \n* [AI Principles: Recommendations on the Ethical Use of Artificial Intelligence](https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF)\n* [Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology](https://media.defense.gov/2020/Jul/01/2002347967/-1/-1/1/DODIG-2020-098.PDF)\n* January 2025 | [Content Credentials: Strengthening Multimedia Integrity in the Generative AI Era](https://media.defense.gov/2025/Jan/29/2003634788/-1/-1/0/CSI-CONTENT-CREDENTIALS.PDF)\n* [Chief Data and Artificial Intelligence Officer (CDAO) Assessment and Assurance](https://gitlab.jatic.net/home)    \n    * [RAI Toolkit](https://rai.tradewindai.com/)\n* Department of the Army\n  * [Proceedings of the Thirteenth Annual U.S. Army Operations Research Symposium, Volume 1, October 29 to November 1, 1974](https://apps.dtic.mil/sti/pdfs/ADA007126.pdf)\n* [Guidelines for secure AI system development](https://media.defense.gov/2023/Nov/27/2003346994/-1/-1/0/GUIDELINES-FOR-SECURE-AI-SYSTEM-DEVELOPMENT.PDF)\n\n**Department of Education**\n* [Office of Educational Technology](https://tech.ed.gov/)\n  * [Designing for Education with Artificial Intelligence: An Essential Guide for Developers](https://tech.ed.gov/designing-for-education-with-artificial-intelligence/)\n  * [Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration, October 2024](https://tech.ed.gov/education-leaders-ai-toolkit/)\n\n**Department of Energy** \n* [Artificial Intelligence and Technology Office](https://www.energy.gov/ai/artificial-intelligence-technology-office)\n  * [AI Risk Management Playbook (AIRMP)](https://www.energy.gov/ai/doe-ai-risk-management-playbook-airmp)\n  * [AI Use Case Inventory (DOE Use Cases Releasable to Public in Accordance with E.O. 13960)](https://www.energy.gov/sites/default/files/2023-07/DOE_2023_AI_Use_Case_Inventory_0.pdf)\n  * [Digital Climate Solutions Inventory](https://www.energy.gov/sites/default/files/2022-09/Digital_Climate_Solutions_Inventory.pdf)\n  * [Generative Artificial Intelligence Reference Guide](https://www.energy.gov/sites/default/files/2024-06/Generative%20AI%20Reference%20Guide%20v2%206-14-24.pdf)\n\n**Department of Homeland Security**  \n* [Artificial Intelligence and Autonomous Systems](https://www.dhs.gov/science-and-technology/artificial-intelligence)\n* [Artificial Intelligence Safety and Security Board](https://www.dhs.gov/artificial-intelligence-safety-and-security-board)\n* [Department of Homeland Security Artificial Intelligence Roadmap 2024](https://www.dhs.gov/sites/default/files/2024-03/24_0315_ocio_roadmap_artificialintelligence-ciov3-signed-508.pdf)\n* [The Department of Homeland Security Simplified Artificial Intelligence Use Case Inventory](https://www.dhs.gov/ai/use-case-inventory)\n  * [AI at DHS: A Deep Dive into our Use Case Inventory](https://www.dhs.gov/news/2024/12/16/ai-dhs-deep-dive-our-use-case-inventory)\n* [DHS Playbook for Public Sector Generative Artificial Intelligence Deployment, January 2025](https://www.dhs.gov/sites/default/files/2025-01/25_0106_ocio_dhs-playbook-for-public-sector-generative-artificial-intelligence-deployment-508-signed.pdf)\n* [Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure, November 14, 2024](https://www.dhs.gov/sites/default/files/2024-11/24_1114_dhs_ai-roles-and-responsibilities-framework-508.pdf)\n* [Safety and Security Guidelines for Critical Infrastructure Owners and Operators](https://www.dhs.gov/publication/safety-and-security-guidelines-critical-infrastructure-owners-and-operators)\n* [Use of Commercial Generative Artificial Intelligence (AI) Tools](https://www.dhs.gov/sites/default/files/2023-11/23_1114_cio_use_generative_ai_tools.pdf)\n\n**Department of Justice**  \n* [Artificial Intelligence Strategy for the U.S. Department of Justice, December 2020](https://www.justice.gov/d9/pages/attachments/2021/02/04/doj_artificial_intelligence_strategy_december_2020.pdf)\n* [Civil Rights Division, Artificial Intelligence and Civil Rights](https://www.justice.gov/crt/ai)\n* [Privacy Act of 1974](https://www.justice.gov/opcl/privacy-act-1974)\n* [Overview of The Privacy Act of 1974 (2020 Edition)](https://www.justice.gov/opcl/overview-privacy-act-1974-2020-edition)\n\n**Department of Labor**\n\n**Department of State**\n* [Artificial Intelligence (AI)](https://www.state.gov/artificial-intelligence/)\n* [AI Inventory 2024 (Archived Content)](https://2021-2025.state.gov/department-of-state-ai-inventory-2024/)\n\n**Department of the Treasury**  \n* [Managing Artificial Intelligence-Specific Cybersecurity Risks in the Financial Services Sector, March 2024](https://home.treasury.gov/system/files/136/Managing-Artificial-Intelligence-Specific-Cybersecurity-Risks-In-The-Financial-Services-Sector.pdf)\n\n**Equal Employment Opportunity Commission (EEOC)**\n* [EEOC Letter (from U.S. senators re: hiring software)](https://www.bennet.senate.gov/public/_cache/files/0/a/0a439d4b-e373-4451-84ed-ba333ce6d1dd/672D2E4304D63A04CC3465C3C8BF1D21.letter-to-chair-dhillon.pdf)\n* [Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines)\n\n**Executive Office of the President of the United States**\n* [Obama White House Archives, Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy, February 2012](https://obamawhitehouse.archives.gov/sites/default/files/privacy-final.pdf)\n* [Framework to Advance AI Governance and Risk Management in National Security](https://ai.gov/wp-content/uploads/2024/10/NSM-Framework-to-Advance-AI-Governance-and-Risk-Management-in-National-Security.pdf)\n\n**Federal Deposit Insurance Corporation (FDIC)**  \n\n**Federal Housing Finance Agency (FHFA)**\n* [Advisory Bulletin AB 2013-07 Model Risk Management Guidance](https://www.fhfa.gov/sites/default/files/2023-03/ab_2013-07_model_risk_management_guidance.pdf)\n\n**Federal Reserve**\n* [Supervisory Guidance on Model Risk Management](https://www.federalreserve.gov/supervisionreg/srletters/sr1107a1.pdf)\n\n**Federal Trade Commission (FTC)**\n* [Artificial Intelligence/Machine Learning (AI/ML)-Based: Software as a Medical Device (SaMD) Action Plan, updated January 2021](https://www.fda.gov/media/145022/download)\n* [Business Blog](https://www.ftc.gov/business-guidance/blog):\n  * [2021-01-11 Facing the facts about facial recognition](https://www.ftc.gov/business-guidance/blog/2021/01/facing-facts-about-facial-recognition)  \n  * [2022-07-11 Location, health, and other sensitive information: FTC committed to fully enforcing the law against illegal use and sharing of highly sensitive data](https://www.ftc.gov/business-guidance/blog/2022/07/location-health-and-other-sensitive-information-ftc-committed-fully-enforcing-law-against-illegal)\n  * [2023-07-25 Protecting the privacy of health information: A baker’s dozen takeaways from FTC cases](https://www.ftc.gov/business-guidance/blog/2023/07/protecting-privacy-health-information-bakers-dozen-takeaways-ftc-cases)\n  * [2023-08-16 Can’t lose what you never had: Claims about digital ownership and creation in the age of generative AI](https://www.ftc.gov/business-guidance/blog/2023/08/cant-lose-what-you-never-had-claims-about-digital-ownership-creation-age-generative-ai)\n  * [2023-08-22 For business opportunity sellers, FTC says “AI” stands for “allegedly inaccurate”](https://www.ftc.gov/business-guidance/blog/2023/08/business-opportunity-sellers-ftc-says-ai-stands-allegedly-inaccurate)\n  * [2023-09-15 Updated FTC-HHS publication outlines privacy and security laws and rules that impact consumer health data](https://www.ftc.gov/business-guidance/blog/2023/09/updated-ftc-hhs-publication-outlines-privacy-security-laws-rules-impact-consumer-health-data)\n  * [2023-09-18 Companies warned about consequences of loose use of consumers’ confidential data](https://www.ftc.gov/business-guidance/blog/2023/09/companies-warned-about-consequences-loose-use-consumers-confidential-data)\n  * [2023-09-27 Could PrivacyCon 2024 be the place to present your research on AI, privacy, or surveillance?](https://www.ftc.gov/business-guidance/blog/2023/09/could-privacycon-2024-be-place-present-your-research-ai-privacy-or-surveillance)\n  * [2022-05-20 Security Beyond Prevention: The Importance of Effective Breach Disclosures](https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2022/05/security-beyond-prevention-importance-effective-breach-disclosures)\n  * [2023-02-01 Security Principles: Addressing underlying causes of risk in complex systems](https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2023/02/security-principles-addressing-underlying-causes-risk-complex-systems)\n  * [2023-06-29 Generative AI Raises Competition Concerns](https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2023/06/generative-ai-raises-competition-concerns)\n  * [2023-12-19 Coming face to face with Rite Aid’s allegedly unfair use of facial recognition technology](https://www.ftc.gov/business-guidance/blog/2023/12/coming-face-face-rite-aids-allegedly-unfair-use-facial-recognition-technology)\n* [Children's Online Privacy Protection Rule (\"COPPA\")](https://www.ftc.gov/legal-library/browse/rules/childrens-online-privacy-protection-rule-coppa)\n* [Privacy Policy](https://www.ftc.gov/policy-notices/privacy-policy)\n* [Software as a Medical Device (SAMD) guidance (December 8, 2017)](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/software-medical-device-samd-clinical-evaluation)\n\n**Food and Drug Administration**\n* [Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations (Draft Guidance for Industry and FDA Staff), January 7, 2025](https://www.fda.gov/media/184856/download)\n\n**Government Accountability Office (GAO)** \n* [Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities, GAO-21-519SP](https://www.gao.gov/products/gao-21-519sp)\n* [Veteran Suicide: VA Efforts to Identify Veterans at Risk through Analysis of Health Record Information](https://www.gao.gov/assets/gao-22-105165.pdf)\n\n**National Security Agency (NSA)**\n* [Central Security Service, Artificial Intelligence Security Center](https://www.nsa.gov/AISC/)\n  \n**National Security Commission on Artificial Intelligence**  \n* [Final Report](https://assets.foleon.com/eu-central-1/de-uploads-7e3kk3/48187/nscai_full_report_digital.04d6b124173c.pdf)\n\n**Office of the Comptroller of the Currency (OCC)**  \n* [2021 Model Risk Management Handbook](https://www.occ.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/index-model-risk-management.html)\n\n**Office of the Director of National Intelligence (ODNI)** \n* [The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines](https://www.dni.gov/files/ODNI/documents/AIM-Strategy.pdf)\n* [Principles of Artificial Intelligence Ethics for the Intelligence Community](https://www.intel.gov/principles-of-artificial-intelligence-ethics-for-the-intelligence-community)\n \n**Securities and Exchange Commission (SEC)**  \n* [Investor Advisory Committee Meeting Agenda for Thursday, March 6, 2025](https://www.sec.gov/about/advisory-committees/investor-advisory-committee/iac030625-agenda)\n* [SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence](https://www.sec.gov/news/press-release/2024-36)\n\n**United States Patent and Trademark Office (USPTO)**\n* [Artificial Intelligence Strategy | January 2025](https://www.uspto.gov/sites/default/files/documents/uspto-ai-strategy.pdf)\n* [Public Views on Artificial Intelligence and Intellectual Property Policy](https://www.uspto.gov/sites/default/files/documents/USPTO_AI-Report_2020-10-07.pdf)\n\n**United States Congress, House of Representatives**\n* [118th Congress, Bipartisan House Task Force Report on Artificial Intelligence, December 2024](https://republicans-science.house.gov/_cache/files/a/a/aa2ee12f-8f0c-46a3-8ff8-8e4215d6a72b/E4AF21104CB138F3127D8FF7EA71A393.ai-task-force-report-final.pdf)\n\n**United States Congress, Senate**\n* [Committee on Commerce, Science, and Transportation, 2024.11.21 Letter to DOJ Re FARA AI Violation (Senator Ted Cruz to Attorney General Merrick Garland)](https://www.commerce.senate.gov/services/files/55267EFF-11A8-4BD6-BE1E-61452A3C48E3)\n\n**United States Web Design System (USWDS)**\n* [Design principles](https://designsystem.digital.gov/design-principles/)\n\n#### United States (State Governments) \n\n**California**\n* [California Consumer Privacy Act (CCPA)](https://oag.ca.gov/privacy/ccpa)\n* [California Department of Justice, How to Read a Privacy Policy](https://www.oag.ca.gov/privacy/facts/online-privacy/privacy-policy)\n* [California Department of Justice, Office of the Attorney General, California Attorney General's Legal Advisory on the Application of Existing California Laws to Artificial Intelligence](https://oag.ca.gov/system/files/attachments/press-docs/Legal%20Advisory%20-%20Application%20of%20Existing%20CA%20Laws%20to%20Artificial%20Intelligence.pdf)\n* [California Department of Technology, GenAI Executive Order](https://cdt.ca.gov/technology-innovation/artificial-intelligence-community/genai-executive-order/)\n* [California Privacy Protection Agency (CPPA), Draft Risk Assessment and Automated Decisionmaking Technology Regulations, March 2024](https://cppa.ca.gov/meetings/materials/20240308_item4_draft_risk.pdf)\n* [Office of the Attorney General of California, California Privacy Rights Act (CPRA)](https://www.oag.ca.gov/system/files/initiatives/pdfs/19-0021A1%20%28Consumer%20Privacy%20-%20Version%203%29_1.pdf)\n\n**Illinois**\n* [Illinois Supreme Court Policy on Artificial Intelligence, Effective January 1, 2025](https://ilcourtsaudio.blob.core.windows.net/antilles-resources/resources/e43964ab-8874-4b7a-be4e-63af019cb6f7/Illinois%20Supreme%20Court%20AI%20Policy.pdf)\n\n**Kentucky**\n* Cabinet for Health and Family Services, February 27, 2025 | [080.101 AI/Gen AI Policy Version 1.1](https://www.chfs.ky.gov/agencies/os/oats/polstand/080101AIGen%20AI.pdf)\n* Legislative Research Commission | [Research Report No. 491 Executive Branch Use of Artificial Intelligence Technology](https://apps.legislature.ky.gov/lrc/publications/ResearchReports/RR491.pdf)\n\n**Mississippi**\n* [Mississippi Department of Education, Artificial Intelligence Guidance for K-12 Classrooms](https://www.mdek12.org/sites/default/files/Offices/MDE/OTSS/DL/ai_guidance_final.pdf)\n\n**New York**\n* [New York City Automated Decision Systems Task Force Report (November 2019)](https://www.nyc.gov/assets/adstaskforce/downloads/pdf/ADS-Report-11192019.pdf)\n* [New York State Emerging Technology Advisory Board: Recommendations for making NY a leader in responsible AI](https://filecache.mediaroom.com/mr5mr_ibmnewsroom/198517/IBM-ETAB-Report-white-paper-DIGITAL-20241212%5B30%5D.pdf)\n* [RE: Use of External Consumer Data and Information Sources in Underwriting for Life Insurance](https://www.dfs.ny.gov/industry_guidance/circular_letters/cl2019_01)\n\n**North Carolina**\n* [North Carolina State Government Responsible Use of Artificial Intelligence Framework, August 2024](https://it.nc.gov/documents/nc-state-government-responsible-use-artificial-intelligence-framework/download?attachment)\n\n**Texas**\n* [Federal Reserve Bank of Dallas, Regulation B, Equal Credit Opportunity, Credit Scoring Interpretations: Withdrawl of Proposed Business Credit Amendments, June 3, 1982](https://fraser.stlouisfed.org/files/docs/historical/frbdal/circulars/frbdallas_circ_19820603_no82-063.pdf)\n\n**Utah**\n* [Questions from the Commission on Protecting Privacy and Preventing Discrimination](https://auditor.utah.gov/wp-content/uploads/sites/6/2021/02/Office-of-the-State-Auditor-Questions-to-help-Procuring-Agencies-_-Entities-with-Software-Procurement-Feb-1-2021-Final.pdf)\n\n**Washington**\n* [Washington Technology Solutions Reports & Documents](https://watech.wa.gov/about/reports-documents)\n  * September 2024 | [Initial procurement guidelines for public sector procurement, deployment, and monitoring of Generative AI Technology](https://watech.wa.gov/sites/default/files/2024-11/Initial%20Procurement%20Guidelines%20for%20GenAI%20Final.pdf)\n  * September 2024 | [State of Washington Generative Artificial Intelligence Report](https://watech.wa.gov/sites/default/files/2024-10/WA_State_GenAIReport_FINAL.pdf)\n  * December 2024 | [Guidelines for Deployment of Generative AI](https://watech.wa.gov/sites/default/files/2024-12/Equity%20Analysis%20Guidelines%20for%20Deployment%20of%20Generative%20AI-WaTech_2024.pdf)\n  * December 2024 | [Implementing risk assessments for high-risk AI systems](https://watech.wa.gov/sites/default/files/2025-01/EO%2024-01%20Risk%20Guidance_Final.pdf)\n  * December 1, 2024 | [Office of Privacy and Data Protection Performance Report](https://watech.wa.gov/sites/default/files/2024-12/OPDP%202024%20Performance%20Report%20Final%2012-1-24.pdf)\n  * January 31, 2025 | [Responsible AI in the Public Sector: How the Washington State Government Uses & Governs Artificial Intelligence](https://watech.wa.gov/sites/default/files/2025-01/Responsible%20AI%20in%20the%20Public%20Sector%20-%20WaTech%20%20UC%20Berkeley%20Report%20-%20Final_.pdf)\n\n**Wyoming**\n* Wyoming Department of Education (WDE) | [Guidance for Wyoming School Districts on Developing Artificial Intelligence Use Policy](https://edu.wyoming.gov/wp-content/uploads/2024/06/Guidance-for-AI-Policy-Development.pdf)\n* Wyoming Department of Education (WDE) | [AI Guidance Resources](https://wde.instructure.com/courses/826)\n\n#### International and Multilateral Frameworks\n\n#### European Union Policies and Regulations\n\n#### Council of Europe\n* [Budapest Centre for Mass Atrocities Prevention, On the Use of Artificial Intelligence in the Framework of the Syrian War, July 2021](https://www.genocideprevention.eu/files/On_the_Use_of_Artificial_Intelligence_in_the_framework_of_the_Syrian_War.pdf)\n* [Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law](https://rm.coe.int/1680afae3c)\n* [European Audiovisual Observatory, IRIS, AI and the audiovisual sector: navigating the current legal landscape](https://rm.coe.int/iris-2024-3-ia-legal-landscape/1680b1e999)\n* [The Framework Convention on Artificial Intelligence](https://www.coe.int/en/web/artificial-intelligence/the-framework-convention-on-artificial-intelligence)\n  * [Explanatory Report to the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law](https://rm.coe.int/1680afae67)\n* [Guidelines on the Responsible Implementation of Artificial Intelligence Systems in Journalism](https://rm.coe.int/cdmsi-2023-014-guidelines-on-the-responsible-implementation-of-artific/1680adb4c6)\n* [Recommendation CM/Rec(2020)1 of the Committee of Ministers to member States on the human rights impacts of algorithmic systems (Adopted by the Committee of Ministers on 8 April 2020 at the 1373rd meeting of the Ministers’ Deputies)](https://search.coe.int/cm?i=09000016809e1154)\n\n#### European Commission and Parliament\n* [Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment - Shaping Europe’s digital future - European Commission](https://ec.europa.eu/digital-single-market/en/news/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment)\n* [Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)](https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence)\n  * [Amendments adopted by the European Parliament on 14 June 2023 on the proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts](https://www.europarl.europa.eu/doceo/document/TA-9-2023-0236_EN.html)\n* [The Digital Services Act package (EU Digital Services Act and Digital Markets Act)](https://digital-strategy.ec.europa.eu/en/policies/digital-services-act-package)\n* [Civil liability regime for artificial intelligence](https://www.europarl.europa.eu/doceo/document/TA-9-2020-0276_EN.pdf)\n* [European Parliament, Addressing AI risks in the workplace: Workers and algorithms](https://www.europarl.europa.eu/thinktank/en/document/EPRS_BRI(2024)762323)\n* [European Parliament, The impact of the General Data Protection Regulation (GDPR) on artificial intelligence](https://www.europarl.europa.eu/RegData/etudes/STUD/2020/641530/EPRS_STU(2020)641530_EN.pdf)\n* European Commission\n  * [Analysis of the preliminary AI standardisation work plan in support of the AI Act](https://publications.jrc.ec.europa.eu/repository/handle/JRC132833)\n  * [Communication from the Commission (4/25/2018), Artificial Intelligence for Europe](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2018%3A237%3AFIN)\n  * [Data Protection Certification Mechanisms: Study on Articles 42 and 43 of the Regulation (EU) 2016/679](https://commission.europa.eu/publications/study-data-protection-certification-mechanisms_en?prefLang=lv)\n  * [Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for Educators](https://school-education.ec.europa.eu/system/files/2023-12/ethical_guidelines_on_the_use_of_artificial_intelligence-nc0722649enn_0.pdf)\n  * [Ethics By Design and Ethics of Use Approaches for Artificial Intelligence, Version 1.0, November 25, 2021](https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ethics-by-design-and-ethics-of-use-approaches-for-artificial-intelligence_he_en.pdf)\n  * [European approach to artificial intelligence](https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence)\n  * [First Draft of the General-Purpose AI Code of Practice published, written by independent experts](https://ec.europa.eu/newsroom/dae/redirection/document/109946)\n  * [Hiroshima Process International Guiding Principles for Advanced AI system](https://digital-strategy.ec.europa.eu/en/library/hiroshima-process-international-guiding-principles-advanced-ai-system)\n  * [Independent High-Level Expert Group on Artificial Intelligence, Ethics Guidelines for Trustworthy AI](https://www.europarl.europa.eu/cmsdata/196377/AI%20HLEG_Ethics%20Guidelines%20for%20Trustworthy%20AI.pdf)\n  * [Independent High-Level Expert Group on Artificial Intelligence, Policy and Investment Recommendations for Trustworthy AI](https://www.europarl.europa.eu/cmsdata/196378/AI%20HLEG_Policy%20and%20Investment%20Recommendations.pdf)\n  * [Living Guidelines on the Responsible Use of Generative AI in Research (ERA Forum Stakeholders' document, First Version, March 2024)](https://research-and-innovation.ec.europa.eu/document/download/2b6cf7e5-36ac-41cb-aab5-0d32050143dc_en?filename=ec_rtd_ai-guidelines.pdf)\n  * [Living repository to foster learning and exchange on AI literacy](https://digital-strategy.ec.europa.eu/en/library/living-repository-foster-learning-and-exchange-ai-literacy)\n    * [Living Repository of AI Literacy Practices v. 31.01.2025](https://ec.europa.eu/newsroom/dae/redirection/document/112203)\n  * [Procurement of AI Updated EU AI model contractual clauses](https://public-buyers-community.ec.europa.eu/communities/procurement-ai/resources/updated-eu-ai-model-contractual-clauses)\n* [Proposal for a directive on adapting non-contractual civil liability rules to artificial intelligence: Complementary impact assessment](https://www.europarl.europa.eu/thinktank/en/document/EPRS_STU(2024)762861)\n\n#### European Council\n* [Artificial intelligence act: Council and Parliament strike a deal on the first rules for AI in the world](https://www.consilium.europa.eu/en/press/press-releases/2023/12/09/artificial-intelligence-act-council-and-parliament-strike-a-deal-on-the-first-worldwide-rules-for-ai/)\n* [Council Conclusions on the Use of Artificial Intelligence in the Field of Justice, December 16, 2024](https://data.consilium.europa.eu/doc/document/ST-16933-2024-INIT/en/pdf)\n\n#### European Data Protection Authorities\n* [Data Protection Authority of Belgium General Secretariat, Artificial Intelligence Systems and the GDPR: A Data Protection Perspective](https://www.autoriteprotectiondonnees.be/publications/artificial-intelligence-systems-and-the-gdpr---a-data-protection-perspective.pdf)\n* [European Data Protection Board (EDPB), AI Auditing documents](https://www.edpb.europa.eu/our-work-tools/our-documents/support-pool-expert-projects/ai-auditing_en)\n* [European Data Protection Board (EDPB), Opinion 28/2024 on certain data protection aspects related to the processing of personal data in the context of AI models](https://www.edpb.europa.eu/system/files/2024-12/edpb_opinion_202428_ai-models_en.pdf)\n* [European Data Protection Supervisor, First EDPS Orientations for EUIs using Generative AI](https://www.edps.europa.eu/data-protection/our-work/publications/guidelines/2024-06-03-first-edps-orientations-euis-using-generative-ai_en)\n* [European Data Protection Supervisor, Generative AI and the EUDPR. First EDPS Orientations for ensuring data protection compliance when using Generative AI systems, June 3, 2024](https://www.edps.europa.eu/system/files/2024-06/24-06-03_genai_orientations_en.pdf)\n* [European Labour Authority (ELA), Artificial Intelligence and Algorithms in Risk Assessment: Addressing Bias, Discrimination and other Legal and Ethical Issues](https://www.ela.europa.eu/sites/default/files/2023-08/ELA-Handbook-AI-training.pdf)\n\n#### OECD\n* [AI, data governance and privacy: Synergies and areas of international co-operation, June 26, 2024](https://www.oecd.org/en/publications/ai-data-governance-and-privacy_2476b1a4-en.html)\n* [Algorithm Impact Assessment Toolkit](https://oecd.ai/en/catalogue/tools/algorithm-impact-assessment-toolkit)\n* [The Bias Assessment Metrics and Measures Repository](https://oecd.ai/en/catalogue/tools/the-bias-assessment-metrics-and-measures-repository)\n* [OECD.AI Catalogue of Tools & Metrics for Trustworthy AI, Anekanta AI, Responsible AI Governance Framework for boards](https://oecd.ai/en/catalogue/tools/responsible-ai-governance-framework-for-boards)\n* [OECD Artificial Intelligence Papers](https://www.oecd-ilibrary.org/science-and-technology/oecd-artificial-intelligence-papers_dee339a8-en)\n  * [No. 1, September 18, 2023, Initial policy considerations for generative artificial intelligence](https://www.oecd-ilibrary.org/deliver/fae2d1e6-en.pdf?itemId=%2Fcontent%2Fpaper%2Ffae2d1e6-en&mimeType=pdf)\n  * [No. 2, October 17, 2023, Emerging trends in AI skill demand across 14 OECD countries](https://www.oecd-ilibrary.org/deliver/7c691b9a-en.pdf?itemId=%2Fcontent%2Fpaper%2F7c691b9a-en&mimeType=pdf)\n  * [No. 3, October 27, 2023, The state of implementation of the OECD AI Principles four years on](https://www.oecd-ilibrary.org/deliver/835641c9-en.pdf?itemId=%2Fcontent%2Fpaper%2F835641c9-en&mimeType=pdf)\n  * [No. 4, October 27, 2023, Stocktaking for the development of an AI incident definition](https://www.oecd-ilibrary.org/deliver/c323ac71-en.pdf?itemId=%2Fcontent%2Fpaper%2Fc323ac71-en&mimeType=pdf)\n  * [No. 5, November 7, 2023, Common guideposts to promote interoperability in AI risk management](https://www.oecd-ilibrary.org/deliver/ba602d18-en.pdf?itemId=%2Fcontent%2Fpaper%2Fba602d18-en&mimeType=pdf)\n  * [No. 6, November 13, 2023, What technologies are at the core of AI?](https://www.oecd-ilibrary.org/deliver/32406765-en.pdf?itemId=%2Fcontent%2Fpaper%2F32406765-en&mimeType=pdf)\n  * [No. 7, November 24, 2023, Using AI to support people with disability in the labour market](https://www.oecd-ilibrary.org/deliver/008b32b7-en.pdf?itemId=%2Fcontent%2Fpaper%2F008b32b7-en&mimeType=pdf)\n  * [No. 8, March 5, 2024, Explanatory memorandum on the updated OECD definition of an AI system](https://www.oecd-ilibrary.org/deliver/623da898-en.pdf?itemId=%2Fcontent%2Fpaper%2F623da898-en&mimeType=pdf)\n  * [No. 9, December 15, 2023, Generative artificial intelligence in finance](https://www.oecd-ilibrary.org/deliver/ac7149cc-en.pdf?itemId=%2Fcontent%2Fpaper%2Fac7149cc-en&mimeType=pdf)\n  * [No. 10, January 19, 2024, Collective action for responsible AI in health](https://www.oecd-ilibrary.org/deliver/f2050177-en.pdf?itemId=%2Fcontent%2Fpaper%2Ff2050177-en&mimeType=pdf)\n  * [No. 11, March 15, 2024, Using AI in the workplace](https://www.oecd-ilibrary.org/deliver/73d417f9-en.pdf?itemId=%2Fcontent%2Fpaper%2F73d417f9-en&mimeType=pdf)\n  * [No. 12, March 22, 2024, Generative AI for anti-corruption and integrity in government](https://www.oecd-ilibrary.org/deliver/657a185a-en.pdf?itemId=%2Fcontent%2Fpaper%2F657a185a-en&mimeType=pdf)\n  * [No. 13, April 10, 2024, Artificial intelligence and wage inequality](https://www.oecd-ilibrary.org/deliver/bf98a45c-en.pdf?itemId=%2Fcontent%2Fpaper%2Fbf98a45c-en&mimeType=pdf)\n  * [No. 14, April 10, 2024, Artificial intelligence and the changing demand for skills in the labour market](https://www.oecd-ilibrary.org/deliver/88684e36-en.pdf?itemId=%2Fcontent%2Fpaper%2F88684e36-en&mimeType=pdf)\n  * [No. 15, April 16, 2024, The impact of Artificial Intelligence on productivity, distribution and growth](https://www.oecd-ilibrary.org/deliver/8d900037-en.pdf?itemId=%2Fcontent%2Fpaper%2F8d900037-en&mimeType=pdf)\n  * [No. 16, May 6, 2024, Defining AI incidents and related terms](https://www.oecd-ilibrary.org/deliver/d1a8d965-en.pdf?itemId=%2Fcontent%2Fpaper%2Fd1a8d965-en&mimeType=pdf)\n  * [No. 17, May 30, 2024, Artificial intelligence and the changing demand for skills in Canada](https://www.oecd-ilibrary.org/deliver/1b20cdb6-en.pdf?itemId=%2Fcontent%2Fpaper%2F1b20cdb6-en&mimeType=pdf)\n  * [No. 18, May 24, 2024, Artificial intelligence, data and competition](https://www.oecd-ilibrary.org/deliver/e7e88884-en.pdf?itemId=%2Fcontent%2Fpaper%2Fe7e88884-en&mimeType=pdf)\n  * [No. 19, June 13, 2024, A new dawn for public employment services](https://www.oecd-ilibrary.org/deliver/5dc3eb8e-en.pdf?itemId=%2Fcontent%2Fpaper%2F5dc3eb8e-en&mimeType=pdf)\n  * [No. 20, June 13, 2024, Governing with Artificial Intelligence](https://www.oecd-ilibrary.org/deliver/26324bc2-en.pdf?itemId=%2Fcontent%2Fpaper%2F26324bc2-en&mimeType=pdf)\n  * [No. 21, June 24, 2024, Using AI to manage minimum income benefits and unemployment assistance](https://www.oecd-ilibrary.org/deliver/718c93a1-en.pdf?itemId=%2Fcontent%2Fpaper%2F718c93a1-en&mimeType=pdf)\n  * [No. 22, June 26, 2024, AI, data governance and privacy](https://www.oecd-ilibrary.org/deliver/2476b1a4-en.pdf?itemId=%2Fcontent%2Fpaper%2F2476b1a4-en&mimeType=pdf)\n  * [No. 23, August 14, 2024, The potential impact of Artificial Intelligence on equity and inclusion in education](https://www.oecd-ilibrary.org/deliver/15df715b-en.pdf?itemId=%2Fcontent%2Fpaper%2F15df715b-en&mimeType=pdf)\n  * [No. 24, September 5, 2024, Regulatory approaches to Artificial Intelligence in finance](https://www.oecd-ilibrary.org/deliver/f1498c02-en.pdf?itemId=%2Fcontent%2Fpaper%2Ff1498c02-en&mimeType=pdf)\n  * [No. 25, September 5, 2024, Measuring the demand for AI skills in the United Kingdom](https://www.oecd-ilibrary.org/deliver/1d6474ef-en.pdf?itemId=%2Fcontent%2Fpaper%2F1d6474ef-en&mimeType=pdf)\n  * [No. 26, October 31, 2024, Who will be the workers most affected by AI?](https://www.oecd-ilibrary.org/deliver/14dc6f89-en.pdf?itemId=%2Fcontent%2Fpaper%2F14dc6f89-en&mimeType=pdf)\n  * [No. 27, November 14, 2024, Assessing potential future artificial intelligence risks, benefits and policy imperatives](https://www.oecd-ilibrary.org/deliver/3f4e3dfb-en.pdf?itemId=%2Fcontent%2Fpaper%2F3f4e3dfb-en&mimeType=pdf)\n  * [No. 28, November 20, 2024, Artificial Intelligence and the health workforce](https://www.oecd-ilibrary.org/deliver/9a31d8af-en.pdf?itemId=%2Fcontent%2Fpaper%2F9a31d8af-en&mimeType=pdf)\n  * [No. 29, November 22, 2024, Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence](https://www.oecd-ilibrary.org/deliver/b524a072-en.pdf?itemId=%2Fcontent%2Fpaper%2Fb524a072-en&mimeType=pdf)\n  * [No. 30, December 12, 2024, A Sectoral Taxonomy of AI Intensity](https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/12/a-sectoral-taxonomy-of-ai-intensity_c2baae71/1f6377b5-en.pdf)\n  * [No. 31, February 6, 2025, Algorithmic Management in the Workplace: New Evidence from an OECD Employer Survey](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/algorithmic-management-in-the-workplace_3c84ed6d/287c13c4-en.pdf)\n  * [No. 32, February 7, 2025, Steering AI's Future: Strategies for Anticipatory Governance](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/steering-ai-s-future_70e4a856/5480ff0a-en.pdf)\n  * [No. 33, February 9, 2025, Intellectual Property Issues in Artificial Intelligence Trained on Scraped Data](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/intellectual-property-issues-in-artificial-intelligence-trained-on-scraped-data_a07f010b/d5241a23-en.pdf)\n  * [No. 34, February 28, 2025, Towards a Common Reporting Framework for AI Incidents](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/towards-a-common-reporting-framework-for-ai-incidents_8c488fdb/f326d4ac-en.pdf)\n  * [No. 35, February 28, 2025, AI Skills and Capabilities in Canada](https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/ai-skills-and-capabilities-in-canada_09294563/87f76682-en.pdf) \n* [OECD-Bericht zu Künstlicher Intelligenz in Deutschland](https://www.ki-strategie-deutschland.de/files/downloads/OECD-Bericht_K%C3%BCnstlicher_Intelligenz_in_Deutschland.pdf)\n* [OECD Digital Economy Papers, No. 341, November 2022, Measuring the Environmental Impacts of Artificial Intelligence Computer and Applications: The AI Footprint](https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/11/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_3dddded5/7babf571-en.pdf)\n* [OECD Legal Instruments, Recommendation of the Council on Artificial Intelligence, adopted May 22, 2019, amended May 3, 2024](https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449)\n* [Measuring the environmental impacts of artificial intelligence compute and applications](https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html)\n\n#### OSCE\n* [#SAIFE Resource Hub: Spotlight on Artificial Intelligence and Freedom of Expression](https://www.osce.org/saife/index.html)\n* [Artificial Intelligence and Disinformation: State-Aligned Information Operations and the Distortion of the Public Sphere, July 2022](https://www.osce.org/files/f/documents/e/b/522166.pdf)\n* [Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual](https://www.osce.org/files/f/documents/8/f/510332_1.pdf)\n\n#### NATO\n* [AI in Precision Persuasion. Unveiling Tactics and Risks on Social Media](https://stratcomcoe.org/publications/ai-in-precision-persuasion-unveiling-tactics-and-risks-on-social-media/309)\n* [Narrative Detection and Topic Modelling in the Baltics](https://stratcomcoe.org/publications/narrative-detection-and-topic-modelling-in-the-baltics/303)\n* [\"NATO-Mation\": Strategies for Leading in the Age of Artificial Intelligence, NDC Research Paper No. 15, December 2020](https://www.ulib.sk/files/english/nato-library/collections/monographs/ndc-research-paper/ndc_rp_15.pdf)\n* [Summary of the NATO Artificial Intelligence Strategy, October 22, 2021](https://www.nato.int/cps/en/natohq/official_texts_187617.htm)\n  * [An Artificial Intelligence Strategy for NATO, October 25, 2021](https://www.nato.int/docu/review/articles/2021/10/25/an-artificial-intelligence-strategy-for-nato/index.html)\n* [Summary of NATO's revised Artificial Intelligence (AI) strategy, July 10, 2024](https://www.nato.int/cps/en/natohq/official_texts_227237.htm)\n\n#### United Nations\n* [Chief Executives Board for Coordination, 2022-10-27, Principles for the ethical use of artificial intelligence in the United Nations system](https://unsceb.org/sites/default/files/2023-03/CEB_2022_2_Add.1%20%28AI%20ethics%20principles%29.pdf)\n* [Governing AI for Humanity, Final Report, September 2024](https://digitallibrary.un.org/record/4062495/files/1416782-EN.pdf?ln=en)\n* [Office for Information and Communications Technology, A Framework for Ethical AI at the United Nations, March 15, 2021](https://unite.un.org/sites/unite.un.org/files/unite_paper_-_ethical_ai_at_the_un.pdf)\n* [Office of the Secretary-General's Envoy on Technology, High-Level Advisory Body on Artificial Intelligence](https://www.un.org/techenvoy/ai-advisory-body)\n* [Office of the United Nations High Commissioner for Human Rights](https://www.ohchr.org/sites/default/files/documents/issues/business/b-tech/taxonomy-GenAI-Human-Rights-Harms.pdf)\n* [UNESCO, Artificial Intelligence: examples of ethical dilemmas](https://www.unesco.org/en/artificial-intelligence/recommendation-ethics/cases)\n* [UNESCO, Caribbean Artificial Intelligence Policy Roadmap](https://unesdoc.unesco.org/ark:/48223/pf0000391996/PDF/391996eng.pdf.multi)\n* [UNESCO, Consultation paper on AI regulation: emerging approaches across the world](https://unesdoc.unesco.org/ark:/48223/pf0000390979)\n* [UNESCO, Global AI Ethics and Governance Observatory](https://www.unesco.org/ethics-ai/en)\n* [UNESCO, Recommendation on the Ethics of Artificial Intelligence, Adopted on 23 November 2021](https://unesdoc.unesco.org/ark:/48223/pf0000381137/PDF/381137eng.pdf.multi)\n * [UNESCO, Readiness assessment methodology: a tool of the Recommendation on the Ethics of Artificial Intelligence](https://www.unesco.org/en/articles/readiness-assessment-methodology-tool-recommendation-ethics-artificial-intelligence)\n* [UNICEF, Policy guidance on AI for children, Version 2.0, November 2021, Recommendations for building AI policies and systems that uphold child rights](https://www.unicef.org/innocenti/media/1341/file/UNICEF-Global-Insight-policy-guidance-AI-children-2.0-2021.pdf)\n\n### Law Texts and Drafts\n\nThis section is a collection of law texts and drafts pertaining to responsible AI.\n\n* [Alaska State Legislature, HB 306, An Act relating to artificial intelligence; requiring disclosure of deepfakes in campaign communications; relating to cybersecurity; and relating to data privacy.](https://www.akleg.gov/basis/Bill/Detail/33?Root=HB306)\n* [Algorithmic Accountability Act of 2023](https://www.govinfo.gov/app/details/BILLS-118hr5628ih/)\n* [Arizona, House Bill 2685](https://www.azleg.gov/legtext/55leg/2r/bills/hb2685h.htm)\n* [Australia, Privacy Act 1988](https://www.legislation.gov.au/Details/C2014C00076)\n* [California, Civil Rights Council - First Modifications to Proposed Employment Regulations on Automated-Decision Systems, Title 2, California Code of Regulations](https://calcivilrights.ca.gov/wp-content/uploads/sites/32/2024/10/First-Modifications-to-Text-of-Proposed-Modifications-to-Employment-Regulations-Regarding-Automated-Decision-Systems.pdf)\n* [California, Consumer Privacy Act of 2018, Civil Code - DIVISION 3. OBLIGATIONS [1427 - 3273.69]](https://leginfo.legislature.ca.gov/faces/codes_displayText.xhtml?division=3.&part=4.&lawCode=CIV&title=1.81.5)\n* [Colorado, SB24-205 Consumer Protections for Artificial Intelligence, Concerning consumer protections in interactions with artificial intelligence systems](https://leg.colorado.gov/bills/SB24-205)\n* [European Union, General Data Protection Regulation (GDPR)](https://gdpr-info.eu/)\n  * [Article 22 EU GDPR \"Automated individual decision-making, including profiling\"](https://www.privacy-regulation.eu/en/article-22-automated-individual-decision-making-including-profiling-GDPR.htm)\n* [Executive Order 13960 (2020-12-03), Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government](https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government)\n* [Facial Recognition and Biometric Technology Moratorium Act of 2020](https://drive.google.com/file/d/1gkTcjFtieMQdsQ01dmDa49B6HY9ZyKr8/view)\n* [Federal Consumer Online Privacy Rights Act (COPRA)](https://www.consumerprivacyact.com/federal/)\n* [Germany, Bundesrat Drucksache 222/24 - Entwurf eines Gesetzes zum strafrechtlichen Schutz von Persönlichkeitsrechten vor Deepfakes (Draft Law on the Criminal Protection of Personality Rights from Deepfakes)](https://www.bundesrat.de/SharedDocs/drucksachen/2024/0201-0300/222-24(B).pdf?__blob=publicationFile&v=1)\n* [Illinois, Biometric Information Privacy Act](https://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=3004&ChapterID=57)\n* [Justice in Policing Act](https://democrats-judiciary.house.gov/issues/issue/?IssueID=14924)\n* [National Conference of State Legislatures (NCSL) 2020 Consumer Data Privacy Legislation](https://www.ncsl.org/technology-and-communication/2020-consumer-data-privacy-legislation)\n* [Nebraska, LB1203 - Regulate artificial intelligence in media and political advertisements under the Nebraska Political Accountability and Disclosure Act](https://nebraskalegislature.gov/bills/view_bill.php?DocumentID=55088)\n* [Rhode Island, Executive Order 24-06: Artificial Intelligence and Data Centers of Excellence](https://governor.ri.gov/executive-orders/executive-order-24-06)\n* [Virginia, Consumer Data Protection Act](https://law.lis.virginia.gov/vacodefull/title59.1/chapter53/)\n* [Washington State, SB 6513 - 2019-20](https://apps.leg.wa.gov/billsummary/?BillNumber=6513&Year=2020&Initiative=false)\n* [United States Congress, 118th Congress (2023-2024), H.R.5586 - DEEPFAKES Accountability Act](https://www.congress.gov/bill/118th-congress/house-bill/5586/text)\n* [United States Congress, 118th Congress (2023-2024), H.R. 9720, AI Incident Reporting and Security Enhancement Act](https://science.house.gov/bills?ID=95D5A008-EA1A-4D43-A363-DC2D129DFDCD)\n* [United States Congress, 118th Congress (2023-2024), S.4769 - VET Artificial Intelligence Act](https://www.congress.gov/bill/118th-congress/senate-bill/4769/text)\n\n## Education Resources\n\n### Comprehensive Software Examples and Tutorials\n\nThis section is a curated collection of guides and tutorials that simplify responsible ML implementation. It spans from basic model interpretability to advanced fairness techniques. Suitable for both novices and experts, the resources cover topics like COMPAS fairness analyses and explainable machine learning via counterfactuals. \n\n* [COMPAS Analysis Using Aequitas](https://github.com/dssg/aequitas/blob/master/docs/source/examples/compas_demo.ipynb)![](https://img.shields.io/github/stars/dssg/aequitas?style=social)\n* [Explaining Quantitative Measures of Fairness (with SHAP)](https://github.com/slundberg/shap/blob/master/notebooks/overviews/Explaining%20quantitative%20measures%20of%20fairness.ipynb)![](https://img.shields.io/github/stars/slundberg/shap?style=social)\n* [Getting a Window into your Black Box Model](http://projects.rajivshah.com/inter/ReasonCode_NFL.html)\n* [H20.ai, From GLM to GBM Part 1](https://www.h2o.ai/blog/from-glm-to-gbm-part-1/)\n* [H20.ai, From GLM to GBM Part 2](https://www.h2o.ai/blog/from-glm-to-gbm-part-2/)\n* [IML](https://mybinder.org/v2/gh/christophM/iml/master?filepath=./notebooks/tutorial-intro.ipynb)\n* [Interpretable Machine Learning with Python](https://github.com/jphall663/interpretable_machine_learning_with_python)![](https://img.shields.io/github/stars/jphall663/interpretable_machine_learning_with_python?style=social)\n* [Interpreting Machine Learning Models with the iml Package](http://uc-r.github.io/iml-pkg)\n* [Interpretable Machine Learning using Counterfactuals](https://docs.seldon.io/projects/alibi/en/v0.2.0/examples/cf_mnist.html)\n* [Machine Learning Explainability by Kaggle Learn](https://www.kaggle.com/learn/machine-learning-explainability)\n* [Model Interpretability with DALEX](http://uc-r.github.io/dalex)\n  * **Model Interpretation series by Dipanjan (DJ) Sarkar**:\n    * [The Importance of Human Interpretable Machine Learning](https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476)\n    * [Model Interpretation Strategies](https://towardsdatascience.com/explainable-artificial-intelligence-part-2-model-interpretation-strategies-75d4afa6b739)\n    * [Hands-on Machine Learning Model Interpretation](https://towardsdatascience.com/explainable-artificial-intelligence-part-3-hands-on-machine-learning-model-interpretation-e8ebe5afc608)\n    * [Interpreting Deep Learning Models for Computer Vision](https://medium.com/google-developer-experts/interpreting-deep-learning-models-for-computer-vision-f95683e23c1d)\n* [Partial Dependence Plots in R](https://journal.r-project.org/archive/2017/RJ-2017-016/)\n* PiML:\n  * [PiML Medium Tutorials](https://piml.medium.com)\n  * [PiML-Toolbox Examples](https://github.com/SelfExplainML/PiML-Toolbox/tree/main/examples)![](https://img.shields.io/github/stars/SelfExplainML/PiML-Toolbox?style=social)\n* [Reliable-and-Trustworthy-AI-Notebooks](https://github.com/ClementSicard/Reliable-and-Trustworthy-AI-Notebooks)![](https://img.shields.io/github/stars/ClementSicard/Reliable-and-Trustworthy-AI-Notebooks?style=social)\n* [Saliency Maps for Deep Learning](https://medium.com/@thelastalias/saliency-maps-for-deep-learning-part-1-vanilla-gradient-1d0665de3284)\n* [Visualizing ML Models with LIME](http://uc-r.github.io/lime)\n* [Visualizing and debugging deep convolutional networks](https://rohitghosh.github.io/2018/01/05/visualising-debugging-deep-neural-networks/)\n* [What does a CNN see?](https://colab.research.google.com/drive/1xM6UZ9OdpGDnHBljZ0RglHV_kBrZ4e-9)\n\n### Free-ish Books\n\nThis section contains books that can be reasonably described as free, including some \"historical\" books dealing broadly with ethical and responsible tech.\n\n* [César A. Hidalgo, Diana Orghian, Jordi Albo-Canals, Filipa de Almeida, and Natalia Martin, 2021, *How Humans Judge Machines*](https://archive.org/details/mit_press_book_9780262363266)\n* [Charles Perrow, 1984, *Normal Accidents: Living with High-Risk Technologies*](https://archive.org/details/normalaccidentsl0000perr)\n* [Charles Perrow, 1999, *Normal Accidents: Living with High-Risk Technologies with a New Afterword and a Postscript on the Y2K Problem*](https://archive.org/details/normalaccidentsl00perr)\n* [Christoph Molnar, 2021, *Interpretable Machine Learning: A Guide for Making Black Box Models Explainable*](https://christophm.github.io/interpretable-ml-book/)\n   * [christophM/interpretable-ml-book](https://github.com/christophM/interpretable-ml-book)![](https://img.shields.io/github/stars/christophM/interpretable-ml-book?style=social)\n* Damian Okaibedi Eke, Kutoma Wakunuma, Simisola Akintoye, and George Ogoh, editors, 2025 | [Trustworthy AI: African Perspectives](https://link.springer.com/book/10.1007/978-3-031-75674-0)\n* [Deborah G. Johnson and Keith W. Miller, 2009, *Computer Ethics: Analyzing Information Technology*, Fourth Edition](https://archive.org/details/computerethicsan0004edjohn)\n* [Ed Dreby and Keith Helmuth (contributors) and Judy Lumb (editor), 2009, *Fueling Our Future: A Dialogue about Technology, Ethics, Public Policy, and Remedial Action*](https://archive.org/details/fuelingourfuture0000unse/mode/2up)\n* [Ethics for people who work in tech](https://ethicsforpeoplewhoworkintech.com/)\n* [Florence G'sell, Regulating under Uncertainty: Governance Options for Generative AI](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4918704)\n* [George Reynolds, 2002, *Ethics in Information Technology*](https://archive.org/details/ethicsininformat00reyn)\n* [George Reynolds, 2002, *Ethics in Information Technology*, Instructor's Edition](https://archive.org/details/ethicsininformat0000reyn)\n* [Joseph Weizenbaum, 1976, *Computer Power and Human Reason: From Judgment to Calculation*](https://archive.org/details/computerpowerhum0000weiz_v0i3/mode/2up)\n* [Kenneth Vaux (editor), 1970, *Who Shall Live? Medicine, Technology, Ethics*](https://archive.org/details/whoshalllivemedi0000hous)\n* [Kush R. Varshney, 2022, *Trustworthy Machine Learning: Concepts for Developing Accurate, Fair, Robust, Explainable, Transparent, Inclusive, Empowering, and Beneficial Machine Learning Systems*](http://www.trustworthymachinelearning.com/)\n* [Marsha Cook Woodbury, 2003, *Computer and Information Ethics*](https://archive.org/details/computerinformat0000wood_q3r6)\n* [M. David Ermann, Mary B. Williams, and Claudio Gutierrez, 1990, *Computers, Ethics, and Society*](https://archive.org/details/computersethicss0000unse)\n* [Morton E. Winston and Ralph D. Edelbach, 2000, *Society, Ethics, and Technology*, First Edition](https://archive.org/details/societyethicstec00wins)\n* [Morton E. Winston and Ralph D. Edelbach, 2003, *Society, Ethics, and Technology*, Second Edition](https://archive.org/details/societyethicstec0000unse)\n* [Morton E. Winston and Ralph D. Edelbach, 2006, *Society, Ethics, and Technology*, Third Edition](https://archive.org/details/societyethicstec00edel)\n* [Nathalie A. Smuha, ed., 2025, The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/0AD007641DE27F837A3A16DBC0888DD1/9781009367813AR.pdf/The_Cambridge_Handbook_of_the_Law__Ethics_and_Policy_of_Artificial_Intelligence.pdf?event-type=FTLA)\n* [Patrick Hall and Navdeep Gill, 2019, *An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI*, Second Edition](https://h2o.ai/content/dam/h2o/en/marketing/documents/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf)\n* [Patrick Hall, Navdeep Gill, and Benjamin Cox, 2021, *Responsible Machine Learning: Actionable Strategies for Mitigating Risks & Driving Adoption*](https://info.h2o.ai/rs/644-PKX-778/images/OReilly_Responsible_ML_eBook.pdf)\n* [Paula Boddington, 2017, *Towards a Code of Ethics for Artificial Intelligence*](https://archive.org/details/towardscodeofeth0000bodd)\n* [Przemyslaw Biecek and Tomasz Burzykowski, 2020, *Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python*](https://ema.drwhy.ai/)\n* [Przemyslaw Biecek, 2023, *Adversarial Model Analysis*](https://ama.drwhy.ai/)\n* [Raymond E. Spier (editor), 2003, *Science and Technology Ethics*](https://archive.org/details/sciencetechnolog0000unse_k7m6)\n* [Richard A. Spinello, 1995, *Ethical Aspects of Information Technology*](https://archive.org/details/ethicalaspectsof00spin)\n* [Richard A. Spinello, 1997, *Case Studies in Information and Computer Ethics*](https://archive.org/details/unset0000unse_l0l0)\n* [Richard A. Spinello, 2003, *Case Studies in Information Technology Ethics*, Second Edition](https://archive.org/details/casestudiesininf02edspin)\n* [Solon Barocas, Moritz Hardt, and Arvind Narayanan, 2022, *Fairness and Machine Learning: Limitations and Opportunities*](https://fairmlbook.org/)\n* [Soraj Hongladarom and Charles Ess, 2007, *Information Technology Ethics: Cultural Perspectives*](https://archive.org/details/informationtechn0000unse_k8c9)\n* [Stephen H. Unger, 1982, *Controlling Technology: Ethics and the Responsible Engineer*, First Edition](https://archive.org/details/controllingtechn0000unge_y4t3)\n* [Stephen H. Unger, 1994, *Controlling Technology: Ethics and the Responsible Engineer*, Second Edition](https://archive.org/details/controllingtechn0000unge)\n\n### Glossaries and Dictionaries\n\nThis section features a collection of glossaries and dictionaries that are geared toward defining terms in ML, including some \"historical\" dictionaries.\n\n* [A.I. For Anyone: The A-Z of AI](https://www.aiforanyone.org/glossary)\n* [The Alan Turing Institute: Data science and AI glossary](https://www.turing.ac.uk/news/data-science-and-ai-glossary)\n* [Appen Artificial Intelligence Glossary](https://appen.com/ai-glossary/)\n* [Artificial intelligence and illusions of understanding in scientific research (glossary on second page)](https://www.nature.com/articles/s41586-024-07146-0.epdf?sharing_token=cbht6Q72InY18AtY6FiVM9RgN0jAjWel9jnR3ZoTv0Ni_LuMWrIZy_SmHlNQlu9tG1u0SCK_wTYxy6bvMe6U_BE3vc5yFmZEpTbIVJozkVYsOei9LdPpNr_wZzvTp4stmzGM54z-riqwhUCk0DD6_YkY_jcgZBnXR8P_8vyFvYpiCtjFrvczN9Lm6NhmrePm)\n* [Brookings: The Brookings glossary of AI and emerging technologies](https://www.brookings.edu/articles/the-brookings-glossary-of-ai-and-emerging-technologies/)\n* [Built In, Responsible AI Explained](https://builtin.com/artificial-intelligence/responsible-ai)\n* [Center for Security and Emerging Technology: Glossary](https://cset.georgetown.edu/glossary/)\n* [Chief Digital and Artificial Intelligence Office (CDAO), Generative Artificial Intelligence Lexicon](https://www.ai.mil/lexicon_ai_terms.html)\n* [CompTIA: Artificial Intelligence (AI) Terminology: A Glossary for Beginners](https://connect.comptia.org/content/articles/artificial-intelligence-terminology)\n* [Council of Europe Artificial Intelligence Glossary](https://www.coe.int/en/web/artificial-intelligence/glossary)\n* [Coursera: Artificial Intelligence (AI) Terms: A to Z Glossary](https://www.coursera.org/articles/ai-terms)\n* [Dataconomy: AI dictionary: Be a native speaker of Artificial Intelligence](https://dataconomy.com/2022/04/23/artificial-intelligence-terms-ai-glossary/)\n* [Dennis Mercadal, 1990, *Dictionary of Artificial Intelligence*](https://archive.org/details/dictionaryofarti0000merc)\n* [European Commission, EU-U.S. Terminology and Taxonomy for Artificial Intelligence - Second Edition](https://digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy-artificial-intelligence-second-edition)\n* [European Commission, Glossary of human-centric artificial intelligence](https://publications.jrc.ec.europa.eu/repository/handle/JRC129614)\n* [G2: 70+ A to Z Artificial Intelligence Terms in Technology](https://www.g2.com/articles/artificial-intelligence-terms)\n* [General Services Administration: AI Guide for Government: Key AI terminology](https://coe.gsa.gov/coe/ai-guide-for-government/what-is-ai-key-terminology/)\n* [Google Developers Machine Learning Glossary](https://developers.google.com/machine-learning/glossary)\n* [H2O.ai Glossary](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/glossary.html)\n* [IAPP Glossary of Privacy Terms](https://iapp.org/resources/glossary/)\n* [IAPP International Definitions of Artificial Intelligence](https://iapp.org/media/pdf/resource_center/international_definitions_of_ai.pdf)\n* [IAPP Key Terms for AI Governance](https://iapp.org/resources/article/key-terms-for-ai-governance/)\n* [IBM AI glossary](https://www.ibm.com/cloud/architecture/architecture/practices/cognitive-glossary/)\n* [IEEE, A Glossary for Discussion of Ethics of Autonomous and Intelligent Systems, Version 1](https://standards.ieee.org/wp-content/uploads/import/documents/other/eadv2_glossary.pdf)\n* [ISO/IEC DIS 22989(en) Information technology — Artificial intelligence — Artificial intelligence concepts and terminology](https://www.iso.org/obp/ui/fr/#iso:std:iso-iec:22989:dis:ed-1:v1:en)\n* [Jerry M. Rosenberg, 1986, *Dictionary of Artificial Intelligence & Robotics*](https://archive.org/details/dictionaryofarti00rose)\n* [MakeUseOf: A Glossary of AI Jargon: 29 AI Terms You Should Know](https://www.makeuseof.com/glossary-ai-jargon-terms/)\n* [Moveworks: AI Terms Glossary](https://www.moveworks.com/us/en/resources/ai-terms-glossary)\n* [National Institute of Standards and Technology (NIST), NIST AI 100-2 E2023: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations](https://csrc.nist.gov/pubs/ai/100/2/e2023/final)\n* [National Institute of Standards and Technology (NIST), The Language of Trustworthy AI: An In-Depth Glossary of Terms](https://airc.nist.gov/AI_RMF_Knowledge_Base/Glossary)\n* [Oliver Houdé, 2004, *Dictionary of Cognitive Science: Neuroscience, Psychology, Artificial Intelligence, Linguistics, and Philosophy*](https://archive.org/details/dictionaryofcogn0000unse)\n* [Open Access Vocabulary](https://repository.ifla.org/bitstream/123456789/3272/1/Open%20Access%20Vocabulary%20Feb2024%20v2.pdf)\n* [Otto Vollnhals, 1992, *A Multilingual Dictionary of Artificial Intelligence (English, German, French, Spanish, Italian)*](https://archive.org/details/multilingualdict0000voll)\n* [Raoul Smith, 1989, *The Facts on File Dictionary of Artificial Intelligence*](https://archive.org/details/factsonfiledicti00smit)\n* [Raoul Smith, 1990, *Collins Dictionary of Artificial Intelligence*](https://archive.org/details/collinsdictionar0000unse_w3w7)\n* [Salesforce: AI From A to Z: The Generative AI Glossary for Business Leaders](https://www.salesforce.com/blog/generative-ai-glossary/)\n* [Siemens, Artificial Intelligence Glossary](https://www.siemens.com/global/en/company/stories/artificial-intelligence/ai-glossary.html)\n* [Stanford University HAI Artificial Intelligence Definitions](https://hai.stanford.edu/sites/default/files/2023-03/AI-Key-Terms-Glossary-Definition.pdf)\n* [TechTarget: Artificial intelligence glossary: 60+ terms to know](https://www.techtarget.com/whatis/feature/Artificial-intelligence-glossary-60-terms-to-know)\n* [TELUS International: 50 AI terms every beginner should know](https://www.telusinternational.com/insights/ai-data/article/50-beginner-ai-terms-you-should-know)\n* [Towards AI, Generative AI Terminology — An Evolving Taxonomy To Get You Started](https://towardsai.net/p/machine-learning/generative-ai-terminology-an-evolving-taxonomy-to-get-you-started)\n* [UK Parliament, Artificial intelligence (AI) glossary](https://post.parliament.uk/artificial-intelligence-ai-glossary/)\n* [University of New South Wales, Bill Wilson, The Machine Learning Dictionary](https://www.cse.unsw.edu.au/~billw/mldict.html)\n* [VAIR (Vocabulary of AI Risks)](https://delaramglp.github.io/vair/)\n* [Wikipedia: Glossary of artificial intelligence](https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence)\n* [William J. Raynor, Jr, 1999, *The International Dictionary of Artificial Intelligence*, First Edition](https://archive.org/details/internationaldic0000rayn/mode/2up)\n* [William J. Raynor, Jr, 2009, *International Dictionary of Artificial Intelligence*, Second Edition](https://archive.org/details/internationaldic0000rayn_t1n5/mode/2up)\n\n### Open-ish Classes\n\nThis section features a selection of educational courses focused on ethical considerations and best practices in ML. The classes range from introductory courses on data ethics to specialized training in fairness and trustworthy deep learning.\n\n* [An Introduction to Data Ethics](https://www.scu.edu/ethics/focus-areas/technology-ethics/resources/an-introduction-to-data-ethics/)\n* [Awesome LLM Courses](https://github.com/wikit-ai/awesome-llm-courses)![](https://img.shields.io/github/stars/wikit-ai/awesome-llm-courses?style=social)\n* [AWS Skill Builder](https://skillbuilder.aws/)\n* [Build a Large Language Model (From Scratch)](https://github.com/rasbt/LLMs-from-scratch/tree/main)![](https://img.shields.io/github/stars/rasbt/LLMs-from-scratch?style=social)\n* [Carnegie Mellon University, Computational Ethics for NLP](http://demo.clab.cs.cmu.edu/ethical_nlp/)\n* [Certified Ethical Emerging Technologist](https://certnexus.com/certification/ceet/)\n* [Coursera, DeepLearning.AI, Generative AI for Everyone](https://www.coursera.org/learn/generative-ai-for-everyone)\n* [Coursera, DeepLearning.AI, Generative AI with Large Language Models](https://www.coursera.org/learn/generative-ai-with-llms)\n* [Coursera, Google Cloud, Introduction to Generative AI](https://www.coursera.org/learn/introduction-to-generative-ai)\n* [Coursera, Vanderbilt University, Prompt Engineering for ChatGPT](https://www.coursera.org/learn/prompt-engineering)\n* [CS103F: Ethical Foundations of Computer Science](https://www.cs.utexas.edu/~ans/classes/cs109/schedule.html)\n* [DeepLearning.AI](https://www.deeplearning.ai/courses/)\n* [ETH Zürich ReliableAI 2022 Course Project repository](https://github.com/angelognazzo/Reliable-Trustworthy-AI)![](https://img.shields.io/github/stars/angelognazzo/Reliable-Trustworthy-AI?style=social)\n* [Fairness in Machine Learning](https://fairmlclass.github.io/)\n* [Fast.ai Data Ethics course](http://ethics.fast.ai/syllabus)\n* [Google Cloud Skills Boost](https://www.cloudskillsboost.google/)\n  * [Attention Mechanism](https://www.cloudskillsboost.google/course_templates/537)\n  * [Create Image Captioning Models](https://www.cloudskillsboost.google/course_templates/542)\n  * [Encoder-Decoder Architecture](https://www.cloudskillsboost.google/course_templates/543)\n  * [Introduction to Generative AI](https://www.cloudskillsboost.google/course_templates/536)\n  * [Introduction to Image Generation](https://www.cloudskillsboost.google/course_templates/541)\n  * [Introduction to Large Language Models](https://www.cloudskillsboost.google/course_templates/539)\n  * [Introduction to Responsible AI](https://www.cloudskillsboost.google/course_templates/554)\n  * [Introduction to Vertex AI Studio](https://www.cloudskillsboost.google/course_templates/552)\n  * [Transformer Models and BERT Model](https://www.cloudskillsboost.google/course_templates/538)\n* [Grow with Google, Generative AI for Educators](https://grow.google/ai-for-educators/)\n* [Human-Centered Machine Learning](http://courses.mpi-sws.org/hcml-ws18/)\n* [IBM SkillsBuild](https://sb-auth.skillsbuild.org/)\n* [Introduction to AI Ethics](https://www.kaggle.com/code/var0101/introduction-to-ai-ethics)\n* [INFO 4270: Ethics and Policy in Data Science](https://docs.google.com/document/d/1GV97qqvjQNvyM2I01vuRaAwHe9pQAZ9pbP7KkKveg1o/)\n* [Introduction to Responsible Machine Learning](https://jphall663.github.io/GWU_rml/)\n* [Jay Alammar, Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/)\n* [Machine Learning Fairness by Google](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)\n* [OECD.AI, Disability-Centered AI And Ethics MOOC](https://oecd.ai/en/catalogue/tools/disability-centered-ai-and-ethics-mooc)\n* [Piotr Sapieżyński's CS 4910 - Special Topics in Computer Science: Algorithm Audits](https://sapiezynski.com/cs4910.html)\n* [Tech & Ethics Curricula](https://docs.google.com/spreadsheets/d/1Z0DqQeZ-Aeq6LmD17J5m8zeeIR6ywWnH-WO-jWtXE9M/edit#gid=0)\n* [Trustworthy Deep Learning](https://berkeley-deep-learning.github.io/cs294-131-s19/)\n\n### Podcasts and Channels\n\nThis section features podcasts and channels (such as on YouTube) that offer insightful commentary and explanations on responsible AI and machine learning interpretability.\n\n* [Internet of Bugs](https://www.youtube.com/@InternetOfBugs/videos)\n* [Tech Won't Save Us](https://techwontsave.us/)\n* [This Is Technology Ethics: An Introduction](https://technologyethicspod.wordpress.com/)\n\n\n## AI Incidents, Critiques, and Research Resources\n\n### AI Incident Information Sharing Resources\n\nThis section houses initiatives, networks, repositories, and publications that facilitate collective and interdisciplinary efforts to enhance AI safety. It includes platforms where experts and practitioners come together to share insights, identify potential vulnerabilities, and collaborate on developing robust safeguards for AI systems, including AI incident trackers.\n\n* [AI Incident Database (Responsible AI Collaborative)](https://incidentdatabase.ai/)\n* [AI Vulnerability Database (AVID)](https://avidml.org/)\n* [AIAAIC](https://www.aiaaic.org/)\n* [AI Badness: An open catalog of generative AI badness](https://badness.ai/)\n* [AI Risk Database](https://airisk.io/)\n* [Atlas of AI Risks](https://social-dynamics.net/atlas/)\n* [Brennan Center for Justice, Artificial Intelligence Legislation Tracker](https://www.brennancenter.org/our-work/research-reports/artificial-intelligence-legislation-tracker)\n* [EthicalTech@GW, Deepfakes & Democracy Initiative](https://blogs.gwu.edu/law-eti/deepfakes-disinformation-democracy/)\n* [George Washington University Law School's AI Litigation Database](https://blogs.gwu.edu/law-eti/ai-litigation-database/)\n* [Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database](https://osf.io/fvqg3/)\n* [Mitre's AI Risk Database](https://github.com/mitre-atlas/ai-risk-database)![](https://img.shields.io/github/stars/mitre-atlas/ai-risk-database?style=social)\n* [OECD AI Incidents Monitor](https://oecd.ai/en/incidents)\n* [Resemble.AI Deepfake Incident Database](https://www.resemble.ai/deepfake-database/)\n* [Verica Open Incident Database (VOID)](https://www.thevoid.community/)\n\n#### Bibliography of Papers on AI Incidents and Failures\n\n* [AI Ethics Issues in Real World: Evidence from AI Incident Database](https://doi.org/10.48550/arXiv.2206.07635)\n* [American Sunlight Project, Deepfake Pornography Goes to Washington: Measuring the Prevalence of AI-Generated Non-Consensual Intimate Imagery Targeting Congress, December 11, 2024](https://static1.squarespace.com/static/6612cbdfd9a9ce56ef931004/t/67586997eaec5c6ae3bb5e24/1733847451191/ASP+DFP+Report.pdf)\n* [The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone Mobile](https://doi.org/10.48550/arXiv.2407.15685)\n* [Artificial Intelligence Incidents & Ethics: A Narrative Review](https://doi.org/10.54489/ijtim.v2i2.80)\n* [Artificial Intelligence Safety and Cybersecurity: A Timeline of AI Failures](https://doi.org/10.48550/arXiv.1610.07997)\n* [Center for Countering Digital Hate (CCDH), YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf)\n* [Deployment Corrections: An Incident Response Framework for Frontier AI Models](https://doi.org/10.48550/arXiv.2310.00328)\n* [Exploring Trust With the AI Incident Database](https://doi.org/10.1177/21695067231198084)\n* [Indexing AI Risks with Incidents, Issues, and Variants](https://doi.org/10.48550/arXiv.2211.10384)\n* [Good Systems, Bad Data?: Interpretations of AI Hype and Failures](https://doi.org/10.1002/pra2.275)\n* [How Does AI Fail Us? A Typological Theorization of AI Failures](https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/25/)\n* [Omission and Commission Errors Underlying AI Failures](https://doi.org/10.1007/s00146-022-01585-x)\n* [Ontologies for Reasoning about Failures in AI Systems](https://mclumd.github.io/ALMECOM%20Papers/2007/Schmill%20et%20al.%20-%202007%20-%20Ontologies%20for%20reasoning%20about%20failures%20in%20AI%20syst.pdf)\n* [Planning for Natural Language Failures with the AI Playbook](https://doi.org/10.1145/3411764.3445735)\n* [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://arxiv.org/abs/2011.08512)\n* [SoK: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security](https://doi.org/10.1145/3664476.3664510)\n* [Understanding and Avoiding AI Failures: A Practical Guide](https://doi.org/10.3390/philosophies6030053)\n* [When Your AI Becomes a Target: AI Security Incidents and Best Practices](https://doi.org/10.1609/aaai.v38i21.30347)\n* [Why We Need to Know More: Exploring the State of AI Incident Documentation Practices](https://dl.acm.org/doi/fullHtml/10.1145/3600211.3604700)\n\n\n### AI Law, Policy, and Guidance Trackers\n\nThis section contains trackers, databases, and repositories of laws, policies, and guidance pertaining to AI.\n\n* [Access Now, Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report](https://www.accessnow.org/wp-content/uploads/2024/07/TRF-LAC-Reporte-Regional-IA-JUN-2024-V3.pdf)\n* [Emerging Technology Observatory, ETO AGORA, AI GOvernance and Regulatory Archive](https://agora.eto.tech/?)\n* [The Ethical AI Database](https://www.eaidb.org/)\n* [George Washington University Law School's AI Litigation Database](https://blogs.gwu.edu/law-eti/ai-litigation-database/)\n* [GobLab UAI, Ethical AI Standards in Chile: Responsible and Transparent Algorithms](https://goblab.uai.cl/en/ethical-algorithms/)\n* [International Association of Privacy Professionals (IAPP), Global AI Legislation Tracker](https://iapp.org/resources/article/global-ai-legislation-tracker/)\n* [International Association of Privacy Professionals (IAPP), UK data protection reform: An overview](https://iapp.org/resources/article/uk-data-protection-reform-an-overview/)\n* [International Association of Privacy Professionals (IAPP), US State Privacy Legislation Tracker](https://iapp.org/resources/article/us-state-privacy-legislation-tracker/)\n* [Institute for the Future of Work, Tracking international legislation relevant to AI at work](https://www.ifow.org/publications/legislation-tracker)\n* [Legal Nodes, Global AI Regulations Tracker: Europe, Americas & Asia-Pacific Overview](https://legalnodes.com/article/global-ai-regulations-tracker)\n* [MIT AI Risk Repository](https://airisk.mit.edu/)\n* [multistate.ai](https://www.multistate.ai/)\n* [National Conference of State Legislatures, Deceptive Audio or Visual Media (‘Deepfakes’) 2024 Legislation](https://www.ncsl.org/technology-and-communication/deceptive-audio-or-visual-media-deepfakes-2024-legislation)\n* [OECD.AI, National AI policies & strategies](https://oecd.ai/en/dashboards/overview)\n* [Raymond Sun, Global AI Regulation Tracker](https://www.techieray.com/GlobalAIRegulationTracker)\n* [Runway Strategies, Global AI Regulation Tracker](https://www.runwaystrategies.co/global-ai-regulation-tracker)\n* [University of North Texas, Artificial Intelligence (AI) Policy Collection](https://digital.library.unt.edu/explore/collections/AIPC/)\n* [VidhiSharma.AI, Global AI Governance Tracker](https://vidhisharmaai.com/global-ai-governance-tracker/)\n* [White & Case, AI Watch: Global regulatory tracker - United States](https://www.whitecase.com/insight-our-thinking/ai-watch-global-regulatory-tracker-united-states)\n\n### Challenges and Competitions\n\nThis section contains challenges and competitions related to responsible ML. \n\n* [FICO Explainable Machine Learning Challenge](https://community.fico.com/s/explainable-machine-learning-challenge)\n* [OSD Bias Bounty](https://osdbiasbounty.com/)\n* [National Fair Housing Alliance Hackathon](https://nationalfairhousing.org/hackathon2023/)\n* [Twitter Algorithmic Bias](https://hackerone.com/twitter-algorithmic-bias?type=team)\n\n### Critiques of AI\n\nThis section contains an assortment of papers, articles, essays, and general resources that take critical stances toward generative AI.\n\n* [Against predictive optimization](https://predictive-optimization.cs.princeton.edu/)\n* [AI can only do 5% of jobs, says MIT economist who fears tech stock crash](https://torontosun.com/business/money-news/ai-can-only-do-5-of-jobs-says-mit-economist-who-fears-tech-stock-crash)\n* [AI chatbots use racist stereotypes even after anti-racism training](https://www.newscientist.com/article/2421067-ai-chatbots-use-racist-stereotypes-even-after-anti-racism-training/)\n* [AI coding assistants do not boost productivity or prevent burnout, study finds](https://www.techspot.com/news/104945-ai-coding-assistants-do-not-boost-productivity-or.html)\n* [AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business](https://link.springer.com/article/10.1007/s43681-024-00443-4)\n* [AI hype, promotional culture, and affective capitalism](https://link.springer.com/article/10.1007/s43681-024-00483-w)\n* [AI Is a Lot of Work](https://nymag.com/intelligencer/article/ai-artificial-intelligence-humans-technology-business-factory.html)\n* [AI is effectively ‘useless’—and it’s created a ‘fake it till you make it’ bubble that could end in disaster, veteran market watcher warns](https://finance.yahoo.com/news/ai-effectively-useless-created-fake-194008129.html)\n* [AI Safety Is a Narrative Problem](https://hdsr.mitpress.mit.edu/pub/wz35dvpo/release/1?readingCollection=3974b7e6)\n* [AI Snake Oil](https://www.aisnakeoil.com/)\n* [AI Tools Still Permitting Political Disinfo Creation, NGO Warns](https://www.barrons.com/news/ai-tools-still-permitting-political-disinfo-creation-ngo-warns-ac791521)\n* [Anthropomorphism in AI: hype and fallacy](https://link.springer.com/article/10.1007/s43681-024-00419-4)\n* [Are Emergent Abilities of Large Language Models a Mirage?](https://arxiv.org/pdf/2304.15004.pdf)\n* [Are Language Models Actually Useful for Time Series Forecasting?](https://arxiv.org/abs/2406.16964v1)\n* [Artificial Hallucinations in ChatGPT: Implications in Scientific Writing](https://assets.cureus.com/uploads/editorial/pdf/138667/20230219-28928-6kcyip.pdf)\n* [Artificial intelligence and illusions of understanding in scientific research](https://rdcu.be/dAw4I)\n* [Artificial Intelligence: Hope for Future or Hype by Intellectuals?](https://ieeexplore.ieee.org/abstract/document/9596410)\n* [Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients](https://qualitysafety.bmj.com/content/early/2024/09/18/bmjqs-2024-017476)\n* [ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs](https://arxiv.org/pdf/2402.11753.pdf)\n* [Aylin Caliskan's publications](https://faculty.washington.edu/aylin/publications.html)\n* [BCS, Does current AI represent a dead end?](https://www.bcs.org/articles-opinion-and-research/does-current-ai-represent-a-dead-end/)\n* [Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks](https://arxiv.org/abs/2407.21072)\n* [Beyond Preferences in AI Alignment](https://arxiv.org/pdf/2408.16984)\n* [Can We Trust AI Agents? An Experimental Study Towards Trustworthy LLM-Based Multi-Agent Systems for AI Ethics](https://arxiv.org/abs/2411.08881)\n* [Center for Countering Digital Hate (CCDH), YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls](https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf)\n* [Chatbots in consumer finance](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/)\n* [ChatGPT is bullshit](https://link.springer.com/article/10.1007/s10676-024-09775-5)\n* [Companies like Google and OpenAI are pillaging the internet and pretending it’s progress](https://bgr.com/business/companies-like-google-and-openai-are-pillaging-the-internet-and-pretending-its-progress/)\n* [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://arxiv.org/abs/2308.08708)\n* [The Cult of AI](https://www.rollingstone.com/culture/culture-features/ai-companies-advocates-cult-1234954528/)\n* [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://www.cell.com/patterns/pdf/S2666-3899(21)00184-7.pdf)\n* [The Data Scientific Method vs. The Scientific Method](https://odsc.com/blog/the-data-scientific-method-vs-the-scientific-method/)\n* [Ed Zitron's Where's Your Ed At](https://www.wheresyoured.at/)\n* [Emergent and Predictable Memorization in Large Language Models](https://arxiv.org/abs/2304.11158)\n* [Evaluating Language-Model Agents on Realistic Autonomous Tasks](https://arxiv.org/pdf/2312.11671.pdf)\n* [FABLES: Evaluating faithfulness and content selection in book-length summarization](https://arxiv.org/abs/2404.01261)\n* [The Fallacy of AI Functionality](https://dl.acm.org/doi/pdf/10.1145/3531146.3533158)\n* [Futurism, Disillusioned Businesses Discovering That AI Kind of Sucks](https://futurism.com/the-byte/businesses-discovering-ai-sucks)\n* [Gen AI: Too Much Spend, Too Little Benefit?](https://www.goldmansachs.com/intelligence/pages/gs-research/gen-ai-too-much-spend-too-little-benefit/report.pdf)\n* [Generative AI: UNESCO study reveals alarming evidence of regressive gender stereotypes](https://www.unesco.org/en/articles/generative-ai-unesco-study-reveals-alarming-evidence-regressive-gender-stereotypes)\n* [Get Ready for the Great AI Disappointment](https://www.wired.com/story/get-ready-for-the-great-ai-disappointment/)\n* [Ghost in the Cloud: Transhumanism’s simulation theology](https://www.nplusonemag.com/issue-28/essays/ghost-in-the-cloud/)\n* [Handling the hype: Implications of AI hype for public interest tech projects](https://www.tatup.de/index.php/tatup/article/view/7080)\n* [The harms of terminology: why we should reject so-called “frontier AI”](https://link.springer.com/article/10.1007/s43681-024-00438-1)\n* [HealthManagement.org, The Journal, Volume 19, Issue 2, 2019, Artificial Hype](https://egve.hu/downloads/health_management/health_management_2019_2_szam.pdf)\n* [How AI hype impacts the LGBTQ + community](https://link.springer.com/article/10.1007/s43681-024-00423-8)\n* [How AI lies, cheats, and grovels to succeed - and what we need to do about it](https://www.zdnet.com/article/how-ai-lies-cheats-and-grovels-to-succeed-and-what-we-need-to-do-about-it/)\n* [Identifying and Eliminating CSAM in Generative ML Training Data and Models](https://stacks.stanford.edu/file/druid:kh752sm9123/ml_training_data_csam_report-2023-12-23.pdf)\n* [Insanely Complicated, Hopelessly Inadequate](https://www.lrb.co.uk/the-paper/v43/n02/paul-taylor/insanely-complicated-hopelessly-inadequate)\n* [Internet of Bugs, Debunking Devin: \"First AI Software Engineer\" Upwork lie exposed!(video)](https://www.youtube.com/watch?v=tNmgmwEtoWE)\n* [It’s Time to Stop Taking Sam Altman at His Word](https://www.theatlantic.com/technology/archive/2024/10/sam-altman-mythmaking/680152/)\n* [I Will Fucking Piledrive You If You Mention AI Again](https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/)\n* [Julia Angwin, Press Pause on the Silicon Valley Hype Machine](https://www.nytimes.com/2024/05/15/opinion/artificial-intelligence-ai-openai-chatgpt-overrated-hype.html)\n* [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://arxiv.org/abs/2401.01301)\n* [Lazy use of AI leads to Amazon products called “I cannot fulfill that request”](https://arstechnica.com/ai/2024/01/lazy-use-of-ai-leads-to-amazon-products-called-i-cannot-fulfill-that-request/)\n* [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://arxiv.org/pdf/2402.03927.pdf)\n* [LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks](https://arxiv.org/pdf/2402.01817.pdf)\n* [Long-context LLMs Struggle with Long In-context Learning](https://huggingface.co/papers/2404.02060)\n* [Low-Resource Languages Jailbreak GPT-4](https://arxiv.org/abs/2310.02446v1)\n* [Machine Learning: The High Interest Credit Card of Technical Debt](https://research.google/pubs/machine-learning-the-high-interest-credit-card-of-technical-debt/)\n* [Measuring the predictability of life outcomes with a scientific mass collaboration](https://www.pnas.org/doi/10.1073/pnas.1915006117)\n* [Medical large language models are vulnerable to data-poisoning attacks](https://www.nature.com/articles/s41591-024-03445-1)\n* [Meta AI Chief: Large Language Models Won't Achieve AGI](https://www.msn.com/en-us/news/technology/meta-ai-chief-large-language-models-won-t-achieve-agi/ar-BB1mRPa5)\n* [Meta’s AI chief: LLMs will never reach human-level intelligence](https://thenextweb.com/news/meta-yann-lecun-ai-behind-human-intelligence)\n* [MIT Technology Review, Introducing: The AI Hype Index](https://www.technologyreview.com/2024/10/23/1105192/ai-hype-index-nov-dec-2024/)\n* [Most CEOs aren’t buying the hype on generative AI benefits](https://www.itpro.com/business/leadership/most-ceos-arent-buying-the-hype-on-generative-ai-benefits)\n* [Nepotistically Trained Generative-AI Models Collapse](https://arxiv.org/abs/2311.12202)\n* [Non-discrimination Criteria for Generative Language Models](https://arxiv.org/abs/2403.08564)\n* [OpenAI—written evidence (LLM0113), House of Lords Communications and Digital Select Committee inquiry: Large language models](https://committees.parliament.uk/writtenevidence/126981/pdf/)\n  * [Former OpenAI Researcher Says the Company Broke Copyright Law](https://www.nytimes.com/2024/10/23/technology/openai-copyright-law.html)\n* [Open Problems in Technical AI Governance](https://arxiv.org/pdf/2407.14981)\n* [On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)\n* [The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’](https://www.nature.com/articles/s41587-023-02103-0)\n* [Pivot to AI](https://pivot-to-ai.com/)\n* [Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models](https://arxiv.org/pdf/2311.00871.pdf)\n* [The Price of Emotion: Privacy, Manipulation, and Bias in Emotional AI](https://www.americanbar.org/groups/business_law/resources/business-law-today/2024-september/price-emotion-privacy-manipulation-bias-emotional-ai/)\n* [Promising the future, encoding the past: AI hype and public media imagery](https://link.springer.com/article/10.1007/s43681-024-00474-x)\n* [Quantifying Memorization Across Neural Language Models](https://arxiv.org/abs/2202.07646)\n* [Re-evaluating GPT-4’s bar exam performance](https://link.springer.com/article/10.1007/s10506-024-09396-9)\n* [Researchers surprised by gender stereotypes in ChatGPT](https://www.dtu.dk/english/news/all-news/researchers-surprised-by-gender-stereotypes-in-chatgpt?id=7e5936d1-dfce-485b-8a90-78f7c757177d)\n* [Ryan Allen, Explainable AI: The What’s and Why’s, Part 1: The What](https://ryanallen42.medium.com/explainable-ai-the-whats-and-why-s-175ea344bf3a)\n* [Sam Altman’s imperial reach](https://www.washingtonpost.com/opinions/2024/10/07/sam-altman-ai-power-danger/)\n* [Scalable Extraction of Training Data from (Production) Language Models](https://arxiv.org/pdf/2311.17035.pdf)\n* [Speed of AI development stretches risk assessments to breaking point](https://www.ft.com/content/499c8935-f46e-4ec8-a8e2-19e07e3b0438)\n* [Talking existential risk into being: a Habermasian critical discourse perspective to AI hype](https://link.springer.com/article/10.1007/s43681-024-00464-z)\n* [Task Contamination: Language Models May Not Be Few-Shot Anymore](https://arxiv.org/pdf/2312.16337.pdf)\n* [Theory Is All You Need: AI, Human Cognition, and Decision Making](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4737265)\n* [There Is No A.I.](https://www.newyorker.com/science/annals-of-artificial-intelligence/there-is-no-ai)\n* [This AI Pioneer Thinks AI Is Dumber Than a Cat](https://www.wsj.com/tech/ai/yann-lecun-ai-meta-aa59e2f5)\n* [Three different types of AI hype in healthcare](https://link.springer.com/article/10.1007/s43681-024-00465-y)\n* [Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context](https://link.springer.com/article/10.1007/s11023-024-09668-y)\n* [We still don't know what generative AI is good for](https://www.msn.com/en-us/news/technology/we-still-dont-know-what-generative-ai-is-good-for/ar-AA1nz1QH)\n* [What’s in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT](https://www.jmir.org/2024/1/e51837/)\n* [Which Humans?](https://osf.io/preprints/psyarxiv/5b26t)\n* [Why the AI Hype is Another Tech Bubble](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4960826)\n* [Why We Must Resist AI’s Soft Mind Control]( https://www.theatlantic.com/ideas/archive/2024/03/artificial-intelligence-google-gemini-mind-control/677683/)\n* [Winner's Curse? On Pace, Progress, and Empirical Rigor](https://openreview.net/pdf?id=rJWF0Fywf)\n\n#### Environmental Costs of AI\n\n* [A bottle of water per email: the hidden environmental costs of using AI chatbots](https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/)\n* [AI already uses as much energy as a small country. It’s only the beginning.](https://www.vox.com/climate/2024/3/28/24111721/ai-uses-a-lot-of-energy-experts-expect-it-to-double-in-just-a-few-years)\n* [The AI Carbon Footprint and Responsibilities of AI Scientists](https://www.mdpi.com/2409-9287/7/1/4)\n* [AI, Climate, and Regulation: From Data Centers to the AI Act](https://arxiv.org/abs/2410.06681)\n* [Artificial Intelligence and Environmental Impact: Moving Beyond Humanizing Vocabulary and Anthropocentrism](https://www.liebertpub.com/doi/abs/10.1089/omi.2024.0197)\n* [Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models](https://dl.acm.org/doi/abs/10.1145/3578337.3605121)\n* [The Carbon Footprint of Artificial Intelligence](https://dl.acm.org/doi/pdf/10.1145/3603746)\n* [The carbon impact of artificial intelligence](https://www.nature.com/articles/s42256-020-0219-9)\n* [Data centre water consumption](https://www.nature.com/articles/s41545-021-00101-w)\n* [Deloitte, Powering artificial intelligence: A study of AI's environmental footprint—today and tomorrow, November 2024](https://www.deloitte.com/content/dam/assets-shared/docs/about/2024/powering-artificial-intelligence.pdf)\n* [Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)](https://www.nature.com/articles/s41599-024-03520-5.pdf)\n* [Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI](https://arxiv.org/abs/2309.02065)\n* [Ensuring a carbon-neutral future for artificial intelligence](https://www.the-innovation.org/data/article/energy/preview/pdf/XINNENERGY-2024-0095.pdf)\n* [Environment and sustainability development: A ChatGPT perspective](https://www.taylorfrancis.com/chapters/oa-edit/10.1201/9781003471059-8/environment-sustainability-development-chatgpt-perspective-priyanka-bhaskar-neha-seth)\n* [The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT](https://puiij.com/index.php/research/article/view/39)\n* [The Environmental Price of Intelligence: Evaluating the Social Cost of Carbon in Machine Learning](https://ieeexplore.ieee.org/abstract/document/10553496)\n* [Generative AI’s environmental costs are soaring — and mostly secret](https://www.nature.com/articles/d41586-024-00478-x)\n* [Green Intelligence Resource Hub](https://docs.google.com/spreadsheets/d/1UCsgAqgonjpP9uPVyssXU0VE0G6Fs7ydxt_mmrpcd1o/edit?usp=sharing)\n* [The growing energy footprint of artificial intelligence](https://www.cell.com/action/showPdf?pii=S2542-4351%2823%2900365-3)\n* [The Hidden Environmental Impact of AI](https://jacobin.com/2024/06/ai-data-center-energy-usage-environment/)\n* [Making AI Less \"Thirsty\": Uncovering and Addressing the Secret Water Footprint of AI Models](https://arxiv.org/abs/2304.03271)\n* [Microsoft’s Hypocrisy on AI](https://www.theatlantic.com/technology/archive/2024/09/microsoft-ai-oil-contracts/679804/)\n* [OECD, Measuring the environmental impacts of artificial intelligence compute and applications](https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html)\n* [The mechanisms of AI hype and its planetary and social costs](https://link.springer.com/article/10.1007/s43681-024-00461-2)\n* [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://dl.acm.org/doi/pdf/10.1145/3630106.3658542)\n* [Promoting Sustainability: Mitigating the Water Footprint in AI-Embedded Data Centres](https://www.igi-global.com/chapter/promoting-sustainability/341617)\n* [Sustainable AI: AI for sustainability and the sustainability of AI](https://link.springer.com/article/10.1007/s43681-021-00043-6)\n* [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://proceedings.mlsys.org/paper_files/paper/2022/file/462211f67c7d858f663355eff93b745e-Paper.pdf)\n* [Toward Responsible AI Use: Considerations for Sustainability Impact Assessment](https://arxiv.org/abs/2312.11996)\n* [Towards A Comprehensive Assessment of AI's Environmental Impact](https://arxiv.org/abs/2405.14004)\n* [Towards Environmentally Equitable AI via Geographical Load Balancing](https://arxiv.org/abs/2307.05494)\n* [Towards green and sustainable artificial intelligence: quantifying the energy footprint of logistic regression and decision tree algorithms](https://ieeexplore.ieee.org/abstract/document/10700922)\n* [Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions](https://www.mdpi.com/2071-1050/14/9/5172)\n\n### Groups and Organizations\n\n* [AI Forum New Zealand, AI Governance Working Group](https://aiforum.org.nz/our-work/working-groups/ai-governance-working-group/)\n* [AI Village](https://aivillage.org/)\n* [Center for Advancing Safety of Machine Intelligence](https://casmi.northwestern.edu/)\n* [Center for AI and Digital Policy](https://www.caidp.org)\n* [Center for Democracy and Technology](https://cdt.org/)\n* [Center for Security and Emerging Technology](https://cset.georgetown.edu/)\n* [Convergence Analysis](https://www.convergenceanalysis.org/about-us)\n* [Future of Life Institute](https://futureoflife.org/)\n* [Indigenous Protocol and Artificial Intelligence Working Group](https://www.indigenous-ai.net/)\n* [Institute for Advanced Study (IAS), AI Policy and Governance Working Group](https://www.ias.edu/stsv-lab/aipolicy)\n* [Institute for Ethics and the Common Good, Notre Dame-IBM Technology Ethics Lab](https://ethics.nd.edu/labs-and-centers/notre-dame-ibm-technology-ethics-lab/)\n* [Partnership on AI](https://partnershiponai.org/)\n    \n### Curated Bibliographies\n\nWe are seeking curated bibliographies related to responsible ML across various topics, see [issue 115](https://github.com/jphall663/awesome-machine-learning-interpretability/issues/115). \n\n* [Blair Attard-Frost, INF1005H1S: Artificial Intelligence Policy Supplementary Reading List](https://www.blairaf.com/library/resources/teaching/2024-INF1005H1S/INF1005-Supplementary-Reading-List.pdf)\n* [Green Intelligence Resource Hub](https://docs.google.com/spreadsheets/d/1UCsgAqgonjpP9uPVyssXU0VE0G6Fs7ydxt_mmrpcd1o/edit?usp=sharing)\n* [Internet Rules Lab, Responsible Computing](https://www.internetruleslab.com/responsible-computing)\n* [LLM Security & Privacy](https://github.com/chawins/llm-sp)![](https://img.shields.io/github/stars/chawins/llm-sp?style=social)\n* [Membership Inference Attacks and Defenses on Machine Learning Models Literature](https://github.com/HongshengHu/membership-inference-machine-learning-literature)![](https://img.shields.io/github/stars/HongshengHu/membership-inference-machine-learning-literature?style=social)\n* [White & Case, AI Watch: Global regulatory tracker - United States](https://www.whitecase.com/insight-our-thinking/ai-watch-global-regulatory-tracker-united-states)\n\n* **BibTeX**:\n  * [Proposed Guidelines for Responsible Use of Explainable Machine Learning (presentation, bibliography)](https://github.com/jphall663/kdd_2019/blob/master/bibliography.bib)![](https://img.shields.io/github/stars/jphall663/kdd_2019?style=social)\n  * [Proposed Guidelines for Responsible Use of Explainable Machine Learning (paper, bibliography)](https://github.com/jphall663/responsible_xai/blob/master/responsible_xai.bib)![](https://img.shields.io/github/stars/jphall663/responsible_xai?style=social)\n  * [A Responsible Machine Learning Workflow (paper, bibliography)](https://github.com/h2oai/article-information-2019/blob/master/back_up/article-information-2019.bib.bak)![](https://img.shields.io/github/stars/h2oai/article-information-2019?style=social)\n \n* **Web**:\n  * [Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship](https://www.fatml.org/resources/relevant-scholarship)\n\n### List of Lists\n\nThis section links to other lists of responsible ML or related resources.\n\n* [A Living and Curated Collection of Explainable AI Methods](https://utwente-dmb.github.io/xai-papers/#/)\n* [AI Ethics Guidelines Global Inventory](https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/)\n* [AI Ethics Resources](https://www.fast.ai/posts/2018-09-24-ai-ethics-resources.html)\n* [AI Tools and Platforms](https://docs.google.com/spreadsheets/u/2/d/10pPQYmyNnYb6zshOKxBjJ704E0XUj2vJ9HCDfoZxAoA/htmlview#)\n* [AthenaCore, Awesome Responsible AI](https://github.com/AthenaCore/AwesomeResponsibleAI)![](https://img.shields.io/github/stars/AthenaCore/AwesomeResponsibleAI?style=social)\n* [Awesome AI Guidelines](https://github.com/EthicalML/awesome-artificial-intelligence-guidelines)![](https://img.shields.io/github/stars/EthicalML/awesome-artificial-intelligence-guidelines?style=social)\n* [Awesome-explainable-AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI/)![](https://img.shields.io/github/stars/wangyongjie-ntu/Awesome-explainable-AI?style=social)\n* [Awesome interpretable machine learning](https://github.com/lopusz/awesome-interpretable-machine-learning)![](https://img.shields.io/github/stars/lopusz/awesome-interpretable-machine-learning?style=social)\n* [awesomelistsio/Awesome AI Ethics](https://github.com/awesomelistsio/awesome-ai-ethics)![](https://img.shields.io/github/stars/awesomelistsio/awesome-ai-ethics?style=social)\n* [Awesome-ML-Model-Governance](https://github.com/visenger/Awesome-ML-Model-Governance)![](https://img.shields.io/github/stars/visenger/Awesome-ML-Model-Governance?style=social)\n* [Awesome MLOps](https://github.com/visenger/awesome-mlops)![](https://img.shields.io/github/stars/visenger/awesome-mlops?style=social)\n* [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-machine-learning-operations)![](https://img.shields.io/github/stars/EthicalML/awesome-machine-learning-operations?style=social)\n* [Awful AI](https://github.com/daviddao/awful-ai)![](https://img.shields.io/github/stars/daviddao/awful-ai?style=social)\n* [Casey Fiesler's AI Ethics & Policy News spreadsheet](https://docs.google.com/spreadsheets/d/11Ps8ILDHH-vojJGyIx7CcaoB5l1mBRHy3OQAgWkm0W4/edit#gid=0)\n* [Chris Kraft's 2024 AI Resources](https://docs.google.com/document/d/1M--GEa5G4pxMHG5FMeUZKbMtIwAtrWJsBtWZTVGVIqI/edit?tab=t.0)\n* [criticalML](https://github.com/rockita/criticalML)![](https://img.shields.io/github/stars/rockita/criticalML?style=social)\n* [Ethics for people who work in tech](https://ethicsforpeoplewhoworkintech.com/)\n* [Evaluation Repository for 'Sociotechnical Safety Evaluation of Generative AI Systems'](https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vQObeTxvXtOs--zd98qG2xBHHuTTJOyNISBJPthZFr3at2LCrs3rcv73d4of1A78JV2eLuxECFXJY43/pubhtml)\n* [IEEE GET Program | GET Program for AI Ethics and Governance Standards](https://ieeexplore.ieee.org/browse/standards/get-program/page/series?id=93)\n* [IMDA-BTG, LLM-Evals-Catalogue](https://github.com/IMDA-BTG/LLM-Evals-Catalogue)![](https://img.shields.io/github/stars/IMDA-BTG/LLM-Evals-Catalogue?style=social)\n* [Machine Learning Ethics References](https://github.com/radames/Machine-Learning-Ethics-References)![](https://img.shields.io/github/stars/radames/Machine-Learning-Ethics-References?style=social)\n* [Machine Learning Interpretability Resources](https://github.com/h2oai/mli-resources)![](https://img.shields.io/github/stars/h2oai/mli-resources?style=social)\n* [MIT AI Agent Index](https://aiagentindex.mit.edu/)\n* [NIST, Comments Received for RFI on Artificial Intelligence Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework/comments-received-rfi-artificial-intelligence-risk-management)\n* [OECD-NIST Catalogue of AI Tools and Metrics](https://oecd.ai/en/catalogue/overview)\n* [OpenAI Cookbook](https://github.com/openai/openai-cookbook/tree/main)![](https://img.shields.io/github/stars/openai/openai-cookbook?style=social)\n* [private-ai-resources](https://github.com/OpenMined/private-ai-resources)![](https://img.shields.io/github/stars/OpenMined/private-ai-resources?style=social)\n* [Ravit Dotan's Resources](https://www.techbetter.ai/resources)\n* [ResponsibleAI](https://romanlutz.github.io/ResponsibleAI/)\n* [Tech & Ethics Curricula](https://docs.google.com/spreadsheets/d/1Z0DqQeZ-Aeq6LmD17J5m8zeeIR6ywWnH-WO-jWtXE9M/edit#gid=0)\n* [Ultraopxt/Awesome AI Ethics & Safety](https://github.com/Ultraopxt/Awesome-AI-Ethics-Safety)![](https://img.shields.io/github/stars/Ultraopxt/Awesome-AI-Ethics-Safety/?style=social)\n* [Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance](https://doi.org/10.1016/j.patter.2023.100857)\n* [Wyoming Department of Education (WDE) | AI Guidance Resources](https://wde.instructure.com/courses/826)\n* [XAI Resources](https://github.com/pbiecek/xai_resources)![](https://img.shields.io/github/stars/pbiecek/xai_resources?style=social)\n* [xaience](https://github.com/andreysharapov/xaience)![](https://img.shields.io/github/stars/andreysharapov/xaience?style=social)\n\n### Platforms\n\n* [Neuronpedia](https://www.neuronpedia.org/)\n\n## Technical Resources\n\n### Benchmarks\n\nThis section contains benchmarks or datasets used for benchmarks for ML systems, particularly those related to responsible ML desiderata.\n\n| Resource | Description |\n| --- | --- |\n| [benchm-ml](https://github.com/szilard/benchm-ml)![](https://img.shields.io/github/stars/szilard/benchm-ml?style=social) | \"A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).\" |\n| [Bias Benchmark for QA dataset (BBQ)](https://github.com/nyu-mll/bbq)![](https://img.shields.io/github/stars/nyu-mll/bbq?style=social) | \"Repository for the Bias Benchmark for QA dataset.\" |\n| [Cataloguing LLM Evaluations](https://github.com/IMDA-BTG/LLM-Evals-Catalogue)![](https://img.shields.io/github/stars/IMDA-BTG/LLM-Evals-Catalogue?style=social) | \"This repository stems from our paper, 'Cataloguing LLM Evaluations,' and serves as a living, collaborative catalogue of LLM evaluation frameworks, benchmarks and papers.\" |\n| [DecodingTrust](https://github.com/AI-secure/DecodingTrust)![](https://img.shields.io/github/stars/huggingface/evaluate?style=social) | \"A Comprehensive Assessment of Trustworthiness in GPT Models.\" |\n| [EleutherAI, Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness)![](https://img.shields.io/github/stars/EleutherAI/lm-evaluation-harness?style=social) | \"A framework for few-shot evaluation of language models.\" |\n| [Evidently AI 100+ LLM benchmarks and evaluation datasets](https://www.evidentlyai.com/llm-evaluation-benchmarks-datasets) | \"A database of LLM benchmarks and datasets to evaluate the performance of language models.\" |\n| [GEM](https://gem-benchmark.com/) | \"GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics.\" |\n| [HELM](https://crfm.stanford.edu/helm/latest/) | \"A holistic framework for evaluating foundation models.\" |\n| [Hugging Face, evaluate](https://github.com/huggingface/evaluate)![](https://img.shields.io/github/stars/huggingface/evaluate?style=social) | \"Evaluate: A library for easily evaluating machine learning models and datasets.\" |\n| [i-gallegos, Fair-LLM-Benchmark](https://github.com/i-gallegos/Fair-LLM-Benchmark)![](https://img.shields.io/github/stars/i-gallegos/Fair-LLM-Benchmark?style=social) | Benchmark from \"Bias and Fairness in Large Language Models: A Survey\" |\n| [jphall663, Generative AI Risk Management Resources](https://github.com/jphall663/gai_risk_management)![](https://img.shields.io/github/stars/jphall663/gai_risk_management?style=social) | \"A place for ideas and drafts related to GAI risk management.\" |\n| [MLCommons, AI Luminate: A collaborative, transparent approach to safer AI](https://mlcommons.org/ailuminate/) | \"The AILuminate v1.1 benchmark suite is the first AI risk assessment benchmark developed with broad involvement from leading AI companies, academia, and civil society.\" |\n| [MLCommons, MLCommons AI Safety v0.5 Proof of Concept](https://mlcommons.org/2024/04/mlc-aisafety-v0-5-poc/) | \"The MLCommons AI Safety Benchmark aims to assess the safety of AI systems in order to guide development, inform purchasers and consumers, and support standards bodies and policymakers.\" |\n| [MLCommons, Introducing v0.5 of the AI Safety Benchmark from MLCommons](https://arxiv.org/pdf/2404.12241.pdf) | A paper about the MLCommons AI Safety Benchmark v0.5. |\n| [Nvidia MLPerf](https://www.nvidia.com/en-us/data-center/resources/mlperf-benchmarks/) | \"MLPerf™ benchmarks—developed by MLCommons, a consortium of AI leaders from academia, research labs, and industry—are designed to provide unbiased evaluations of training and inference performance for hardware, software, and services.\" | \n| [OpenML Benchmarking Suites](https://www.openml.org/search?type=benchmark&study_type=task) | OpenML's collection of over two dozen benchmarking suites. |\n| [Real Toxicity Prompts (Allen Institute for AI)](https://allenai.org/data/real-toxicity-prompts) | \"A dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.\" |\n| [SafetyPrompts.com](https://safetyprompts.com/) | \"A Living Catalogue of Open Datasets for LLM Safety.\" |\n| [Sociotechnical Safety Evaluation Repository](https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vQObeTxvXtOs--zd98qG2xBHHuTTJOyNISBJPthZFr3at2LCrs3rcv73d4of1A78JV2eLuxECFXJY43/pubhtml) | An extensive spreadsheet of sociotechnical safety evaluations in a spreadsheet. |\n| [TrustLLM-Benchmark](https://trustllmbenchmark.github.io/TrustLLM-Website/index.html) | \"A Comprehensive Study of Trustworthiness in Large Language Models.\" |\n| [Trust-LLM-Benchmark Leaderboard](https://trustllmbenchmark.github.io/TrustLLM-Website/leaderboard.html) | A series of sortable leaderboards of LLMs based on different trustworthiness criteria. |\n| [TruthfulQA](https://github.com/sylinrl/TruthfulQA)![](https://img.shields.io/github/stars/sylinrl/TruthfulQA?style=social) | \"TruthfulQA: Measuring How Models Imitate Human Falsehoods.\" |\n| [WAVES: Benchmarking the Robustness of Image Watermarks](https://wavesbench.github.io/) | \"This paper investigates the weaknesses of image watermarking techniques.\" |\n| [Wild-Time: A Benchmark of in-the-Wild Distribution Shifts over Time](https://github.com/huaxiuyao/Wild-Time)![](https://img.shields.io/github/stars/huaxiuyao/Wild-Time?style=social) | \"Benchmark for Natural Temporal Distribution Shift (NeurIPS 2022).\" |\n| [Winogender Schemas](https://github.com/rudinger/winogender-schemas)![](https://img.shields.io/github/stars/rudinger/winogender-schemas?style=social) | \"Data for evaluating gender bias in coreference resolution systems.\" |\n| [yandex-research / tabred](https://github.com/yandex-research/tabred)![](https://img.shields.io/github/stars/yandex-research/tabred?style=social) | \"A Benchmark of Tabular Machine Learning in-the-Wild with real-world industry-grade tabular datasets.\" |\n\n\n### Common or Useful Datasets\n\nThis section contains datasets that are commonly used in responsible ML evaulations or repositories of interesting/important data sources:\n\n* [Adult income dataset](https://www.kaggle.com/datasets/wenruliu/adult-income-dataset)\n* [Balanced Faces in the Wild](https://github.com/visionjo/facerec-bias-bfw)![](https://img.shields.io/github/stars/visionjo/facerec-bias-bfw?style=social)\n* [Bruegel, A dataset on EU legislation for the digital world](https://www.bruegel.org/dataset/dataset-eu-legislation-digital-world)\n* [COMPAS Recidivism Risk Score Data and Analysis](https://www.propublica.org/datastore/dataset/compas-recidivism-risk-score-data-and-analysis)\n  * **Data Repositories**:\n    * [All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club)\n    * [Amazon Open Data](https://registry.opendata.aws/amazon-reviews/)\n    * [Data.gov](https://data.gov/)\n    * [Home Mortgage Disclosure Act (HMDA) Data](https://www.consumerfinance.gov/data-research/hmda/)\n    * [MIMIC-III Clinical Database](https://physionet.org/content/mimiciii/1.4/)\n    * [UCI ML Data Repository](https://archive.ics.uci.edu/)\n* [FANNIE MAE Single Family Loan Performance](https://capitalmarkets.fanniemae.com/credit-risk-transfer/single-family-credit-risk-transfer/fannie-mae-single-family-loan-performance-data)\n* [Have I Been Trained?](https://haveibeentrained.com/)\n* [nikhgarg / EmbeddingDynamicStereotypes](https://github.com/nikhgarg/EmbeddingDynamicStereotypes)![](https://img.shields.io/github/stars/nikhgarg/EmbeddingDynamicStereotypes?style=social)\n* [Presidential Deepfakes Dataset](https://www.media.mit.edu/publications/presidential-deepfakes-dataset/)\n* [NYPD Stop, Question and Frisk Data](https://www.nyc.gov/site/nypd/stats/reports-analysis/stopfrisk.page)\n* [socialfoundations / folktables](https://github.com/socialfoundations/folktables)![](https://img.shields.io/github/stars/socialfoundations/folktables?style=social)\n* [Statlog (German Credit Data)](https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data)\n* [Wikipedia Talk Labels: Personal Attacks](https://www.kaggle.com/datasets/jigsaw-team/wikipedia-talk-labels-personal-attacks)\n\n### Domain-specific Software\n\nThis section curates specialized software tools aimed at responsible ML within specific domains, such as in healthcare, finance, or social sciences.\n\n### Machine Learning Environment Management Tools\n\nThis section contains open source or open access ML environment management software.\n\n| Resource | Description |\n|----------|-------|\n| [dvc](https://dvc.org/) | \"Manage and version images, audio, video, and text files in storage and organize your ML modeling process into a reproducible workflow.\" |\n| [gigantum](https://github.com/gigantum)![gigantum stars](https://img.shields.io/github/stars/gigantum?style=social) | \"Building a better way to create, collaborate, and share data-driven science.\" |\n| [mlflow](https://mlflow.org/) | \"An open source platform for the machine learning lifecycle.\" |\n| [mlmd](https://github.com/google/ml-metadata)![mlmd stars](https://img.shields.io/github/stars/google/ml-metadata?style=social) | \"For recording and retrieving metadata associated with ML developer and data scientist workflows.\" |\n| [modeldb](https://github.com/VertaAI/modeldb)![modeldb stars](https://img.shields.io/github/stars/VertaAI/modeldb?style=social) | \"Open Source ML Model Versioning, Metadata, and Experiment Management.\" |\n| [neptune](https://neptune.ai/researchers) | \"A single place to manage all your model metadata.\" |\n| [Opik](https://github.com/comet-ml/opik)![](https://img.shields.io/github/stars/comet-ml/opik?style=social) |  \"Evaluate, test, and ship LLM applications across your dev and production lifecycles.\" |\n\n### Personal Data Protection Tools\n\nThis section contains tools for personal data protection.\n\n| Name | Description |\n|------|-------------|\n| [LLM Dataset Inference: Did you train on my dataset?](https://github.com/pratyushmaini/llm_dataset_inference/)![](https://img.shields.io/github/stars/pratyushmaini/llm_dataset_inference?style=social) | \"Official Repository for Dataset Inference for LLMs\" |\n\n### Open Source/Access Responsible AI Software Packages\n\nThis section contains open source or open access software used to implement responsible ML. As much as possible, descriptions are quoted verbatim from the respective repositories themselves. In rare instances, we provide our own descriptions (unmarked by quotes).\n\n#### Browser\n\n| Name | Description |\n|------|-------------|\n| [DiscriLens](https://github.com/wangqianwen0418/DiscriLens)![](https://img.shields.io/github/stars/wangqianwen0418/DiscriLens?style=social) | \"Discrimination in Machine Learning.\" |\n| [Hugging Face, BiasAware: Dataset Bias Detection](https://huggingface.co/spaces/avid-ml/biasaware) | \"BiasAware is a specialized tool for detecting and quantifying biases within datasets used for Natural Language Processing (NLP) tasks.\" |\n| [manifold](https://github.com/uber/manifold)![](https://img.shields.io/github/stars/uber/manifold?style=social) | \"A model-agnostic visual debugging tool for machine learning.\" |\n| [PAIR-code / datacardsplaybook](https://github.com/PAIR-code/datacardsplaybook)![](https://img.shields.io/github/stars/PAIR-code/datacardsplaybook?style=social) | \"The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation.\" |\n| [PAIR-code / facets](https://github.com/PAIR-code/facets)![](https://img.shields.io/github/stars/PAIR-code/facets?style=social) | \"Visualizations for machine learning datasets.\" |\n| [PAIR-code / knowyourdata](https://github.com/pair-code/knowyourdata)![](https://img.shields.io/github/stars/PAIR-code/knowyourdata?style=social) | \"A tool to help researchers and product teams understand datasets with the goal of improving data quality, and mitigating fairness and bias issues.\" |\n| [TensorBoard Projector](http://projector.tensorflow.org) | \"Using the TensorBoard Embedding Projector, you can graphically represent high dimensional embeddings. This can be helpful in visualizing, examining, and understanding your embedding layers.\" |\n| [What-if Tool](https://pair-code.github.io/what-if-tool/index.html#about) | \"Visually probe the behavior of trained machine learning models, with minimal coding.\" |\n\n#### C/C++\n\n| Name | Description |\n|------|-------------|\n| [Born-again Tree Ensembles](https://github.com/vidalt/BA-Trees)![](https://img.shields.io/github/stars/vidalt/BA-Trees?style=social) | \"Born-Again Tree Ensembles: Transforms a random forest into a single, minimal-size, tree with exactly the same prediction function in the entire feature space (ICML 2020).\" |\n| [Certifiably Optimal RulE ListS](https://github.com/nlarusstone/corels)![](https://img.shields.io/github/stars/nlarusstone/corels?style=social) | \"CORELS is a custom discrete optimization technique for building rule lists over a categorical feature space.\" |\n| [Secure-ML](https://github.com/shreya-28/Secure-ML)![](https://img.shields.io/github/stars/shreya-28/Secure-ML?style=social) | \"Secure Linear Regression in the Semi-Honest Two-Party Setting.\" |\n\n#### JavaScript\n\n| Name | Description |\n|------|-------------|\n| [LDNOOBW](https://github.com/LDNOOBW)![](https://img.shields.io/github/stars/LDNOOBW?style=social) | \"List of Dirty, Naughty, Obscene, and Otherwise Bad Words\" |\n\n#### Python\n\n| Name | Description |\n|------|-------------|\n| [acd](https://github.com/csinva/hierarchical_dnn_interpretations)![](https://img.shields.io/github/stars/csinva/hierarchical_dnn_interpretations?style=social) | \"Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code for *Hierarchical interpretations for neural network predictions*.” |\n| [aequitas](https://github.com/dssg/aequitas)![](https://img.shields.io/github/stars/dssg/aequitas?style=social) | \"Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive tools.” |\n| [AI Fairness 360](https://github.com/Trusted-AI/AIF360)![](https://img.shields.io/github/stars/Trusted-AI/AIF360?style=social) | \"A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.” |\n| [AI Explainability 360](https://github.com/IBM/AIX360)![](https://img.shields.io/github/stars/IBM/AIX360?style=social) | \"Interpretability and explainability of data and machine learning models.” |\n| [ALEPython](https://github.com/blent-ai/ALEPython)![](https://img.shields.io/github/stars/blent-ai/ALEPython?style=social) | \"Python Accumulated Local Effects package.” |\n| [Aletheia](https://github.com/SelfExplainML/Aletheia)![](https://img.shields.io/github/stars/SelfExplainML/Aletheia?style=social) | \"A Python package for unwrapping ReLU DNNs.” |\n| [allennlp](https://github.com/allenai/allennlp)![](https://img.shields.io/github/stars/allenai/allennlp?style=social) | \"An open-source NLP research library, built on PyTorch.” |\n| [algofairness](https://github.com/algofairness)![](https://img.shields.io/github/stars/algofairness?style=social) | See [Algorithmic Fairness][http://fairness.haverford.edu/). |\n| [Alibi](https://github.com/SeldonIO/alibi)![](https://img.shields.io/github/stars/SeldonIO/alibi?style=social) | \"Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.” |\n| [anchor](https://github.com/marcotcr/anchor)![](https://img.shields.io/github/stars/marcotcr/anchor?style=social) | \"Code for 'High-Precision Model-Agnostic Explanations' paper.” |\n| [Bayesian Case Model](https://users.cs.duke.edu/~cynthia/code/BCM.zip) |\n| [Bayesian Ors-Of-Ands](https://github.com/wangtongada/BOA)![](https://img.shields.io/github/stars/wangtongada/BOA?style=social) | \"This code implements the Bayesian or-of-and algorithm as described in the BOA paper. We include the tictactoe dataset in the correct formatting to be used by this code.” |\n| [Bayesian Rule List (BRL)](https://users.cs.duke.edu/~cynthia/code/BRL_supplement_code.zip) | Rudin group at Duke Bayesian case model implementation |\n| [BlackBoxAuditing](https://github.com/algofairness/BlackBoxAuditing)![](https://img.shields.io/github/stars/algofairness/BlackBoxAuditing?style=social) | \"Research code for auditing and exploring black box machine-learning models.” |\n| [CalculatedContent, WeightWatcher](https://github.com/calculatedcontent/weightwatcher)![](https://img.shields.io/github/stars/calculatedcontent/weightwatcher?style=social) | \"The WeightWatcher tool for predicting the accuracy of Deep Neural Networks.\" |\n| [casme](https://github.com/kondiz/casme)![](https://img.shields.io/github/stars/kondiz/casme?style=social) | \"contains the code originally forked from the ImageNet training in PyTorch that is modified to present the performance of classifier-agnostic saliency map extraction, a practical algorithm to train a classifier-agnostic saliency mapping by simultaneously training a classifier and a saliency mapping.” |\n| [Causal Discovery Toolbox](https://github.com/FenTechSolutions/CausalDiscoveryToolbox)![](https://img.shields.io/github/stars/FenTechSolutions/CausalDiscoveryToolbox?style=social) | \"Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.” |\n| [captum](https://github.com/pytorch/captum)![](https://img.shields.io/github/stars/pytorch/captum?style=social) | \"Model interpretability and understanding for PyTorch.” |\n| [causalml](https://github.com/uber/causalml)![](https://img.shields.io/github/stars/uber/causalml?style=social) | \"Uplift modeling and causal inference with machine learning algorithms.” |\n| [cdt15, Causal Discovery Lab., Shiga University](https://github.com/cdt15)![](https://img.shields.io/github/stars/cdt15?style=social) | \"LiNGAM is a new method for estimating structural equation models or linear causal Bayesian networks. It is based on using the non-Gaussianity of the data.\" |\n| [checklist](https://github.com/marcotcr/checklist)![](https://img.shields.io/github/stars/marcotcr/checklist?style=social) | \"Beyond Accuracy: Behavioral Testing of NLP models with CheckList.” |\n| [cleverhans](https://github.com/cleverhans-lab/cleverhans)![](https://img.shields.io/github/stars/cleverhans-lab/cleverhans?style=social) | \"An adversarial example library for constructing attacks, building defenses, and benchmarking both.” |\n| [contextual-AI](https://github.com/SAP/contextual-ai)![](https://img.shields.io/github/stars/SAP/contextual-ai?style=social) | \"Contextual AI adds explainability to different stages of machine learning pipelines | data, training, and inference | thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.” |\n| [ContrastiveExplanation (Foil Trees)](https://github.com/MarcelRobeer/ContrastiveExplanation)![](https://img.shields.io/github/stars/MarcelRobeer/ContrastiveExplanation?style=social) | \"provides an explanation for why an instance had the current outcome (fact) rather than a targeted outcome of interest (foil). These counterfactual explanations limit the explanation to the features relevant in distinguishing fact from foil, thereby disregarding irrelevant features.” |\n| [counterfit](https://github.com/Azure/counterfit/)![](https://img.shields.io/github/stars/Azure/counterfit?style=social) | \"a CLI that provides a generic automation layer for assessing the security of ML models.” |\n| [dalex](https://github.com/ModelOriented/DALEX)![](https://img.shields.io/github/stars/ModelOriented/DALEX?style=social) | \"moDel Agnostic Language for Exploration and eXplanation.” |\n| [debiaswe](https://github.com/tolga-b/debiaswe)![](https://img.shields.io/github/stars/tolga-b/debiaswe?style=social) | \"Remove problematic gender bias from word embeddings.” |\n| [DeepExplain](https://github.com/marcoancona/DeepExplain)![](https://img.shields.io/github/stars/marcoancona/DeepExplain?style=social) | \"provides a unified framework for state-of-the-art gradient and perturbation-based attribution methods. It can be used by researchers and practitioners for better undertanding the recommended existing models, as well for benchmarking other attribution methods.” |\n| [DeepLIFT](https://github.com/kundajelab/deeplift)![](https://img.shields.io/github/stars/kundajelab/deeplift?style=social) | \"This repository implements the methods in 'Learning Important Features Through Propagating Activation Differences' by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, gradient-times-input (equivalent to a version of Layerwise Relevance Propagation for ReLU networks), guided backprop and integrated gradients.” |\n| [deepvis](https://github.com/yosinski/deep-visualization-toolbox)![](https://img.shields.io/github/stars/yosinski/deep-visualization-toolbox?style=social) | \"the code required to run the Deep Visualization Toolbox, as well as to generate the neuron-by-neuron visualizations using regularized optimization.” |\n| [DIANNA](https://github.com/dianna-ai/dianna)![](https://img.shields.io/github/stars/dianna-ai/dianna?style=social) | \"DIANNA is a Python package that brings explainable AI (XAI) to your research project. It wraps carefully selected XAI methods in a simple, uniform interface. It's built by, with and for (academic) researchers and research software engineers working on machine learning projects.” |\n| [DiCE](https://github.com/interpretml/DiCE)![](https://img.shields.io/github/stars/interpretml/DiCE?style=social) | \"Generate Diverse Counterfactual Explanations for any machine learning model.” |\n| [DoWhy](https://github.com/microsoft/dowhy)![](https://img.shields.io/github/stars/microsoft/dowhy?style=social) | \"DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.” |\n| [dtreeviz](https://github.com/parrt/dtreeviz)![](https://img.shields.io/github/stars/parrt/dtreeviz?style=social) | \"A python library for decision tree visualization and model interpretation.” |\n| [ecco](https://github.com/jalammar/ecco)![](https://img.shields.io/github/stars/jalammar/ecco?style=social) | \"Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).” |\n| [effector](https://github.com/givasile/effector)![](https://img.shields.io/github/stars/givasile/effector?style=social) | \"eXplainable AI for Tabular Data\" |\n| [eli5](https://github.com/TeamHG-Memex/eli5)![](https://img.shields.io/github/stars/TeamHG-Memex/eli5?style=social) | \"A library for debugging/inspecting machine learning classifiers and explaining their predictions.” |\n| [explabox](https://github.com/MarcelRobeer/explabox)![](https://img.shields.io/github/stars/MarcelRobeer/explabox?style=social) | \"aims to support data scientists and machine learning (ML) engineers in explaining, testing and documenting AI/ML models, developed in-house or acquired externally. The explabox turns your ingestibles (AI/ML model and/or dataset) into digestibles (statistics, explanations or sensitivity insights).” |\n| [Explainable Boosting Machine (EBM)/GA2M](https://github.com/interpretml/interpret)![](https://img.shields.io/github/stars/interpretml/interpret?style=social) | \"an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.” |\n| [ExplainaBoard](https://github.com/neulab/ExplainaBoard)![](https://img.shields.io/github/stars/neulab/ExplainaBoard?style=social) | \"a tool that inspects your system outputs, identifies what is working and what is not working, and helps inspire you with ideas of where to go next.” |\n| [explainerdashboard](https://github.com/oegedijk/explainerdashboard)![](https://img.shields.io/github/stars/oegedijk/explainerdashboard?style=social) | \"Quickly build Explainable AI dashboards that show the inner workings of so-called \"blackbox\" machine learning models.” |\n| [explainX](https://github.com/explainX/explainx)![](https://img.shields.io/github/stars/explainX/explainx?style=social) | \"Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.” |\n| [fair-classification](https://github.com/mbilalzafar/fair-classification)![](https://img.shields.io/github/stars/mbilalzafar/fair-classification?style=social) | \"Python code for training fair logistic regression classifiers.” |\n| [fairml](https://github.com/adebayoj/fairml)![](https://img.shields.io/github/stars/adebayoj/fairml?style=social) | \"a python toolbox auditing the machine learning models for bias.” |\n| [fairlearn](https://github.com/fairlearn/fairlearn)![](https://img.shields.io/github/stars/fairlearn/fairlearn?style=social) | \"a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage.” |\n| [fairness-comparison](https://github.com/algofairness/fairness-comparison)![](https://img.shields.io/github/stars/algofairness/fairness-comparison?style=social) | \"meant to facilitate the benchmarking of fairness aware machine learning algorithms.” |\n| [fairness_measures_code](https://github.com/megantosh/fairness_measures_code)![](https://img.shields.io/github/stars/megantosh/fairness_measures_code?style=social) | \"contains implementations of measures used to quantify discrimination.” |\n| [Falling Rule List (FRL)](https://users.cs.duke.edu/~cynthia/code/falling_rule_list.zip) | Rudin group at Duke falling rule list implementation |\n| [foolbox](https://github.com/bethgelab/foolbox)![](https://img.shields.io/github/stars/bethgelab/foolbox?style=social) | \"A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX.” |\n| [Giskard](https://github.com/Giskard-AI/giskard)![](https://img.shields.io/github/stars/Giskard-AI/giskard?style=social) | \"The testing framework dedicated to ML models, from tabular to LLMs. Scan AI models to detect risks of biases, performance issues and errors. In 4 lines of code.” |\n| [Grad-CAM](https://github.com/topics/grad-cam) (GitHub topic) | Grad-CAM is a technique for making convolutional neural networks more transparent by visualizing the regions of input that are important for predictions in computer vision models. |\n| [gplearn](https://github.com/trevorstephens/gplearn)![](https://img.shields.io/github/stars/trevorstephens/gplearn?style=social) | \"implements Genetic Programming in Python, with a scikit-learn inspired and compatible API.” |\n| [H2O-3](https://github.com/h2oai/h2o-3) [Penalized Generalized Linear Models](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlinearestimator) | \"Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.\" |\n| [H2O-3](https://github.com/h2oai/h2o-3) [Monotonic GBM](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogradientboostingestimator) | \"Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data set.\" |\n| [H2O-3](https://github.com/h2oai/h2o-3) [Sparse Principal Components (GLRM)](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlowrankestimator) | \"Builds a generalized low rank decomposition of an H2O data frame.\" |\n| [h2o-LLM-eval](https://github.com/h2oai/h2o-LLM-eval)![](https://img.shields.io/github/stars/h2oai/h2o-LLM-eval?style=social) | \"Large-language Model Evaluation framework with Elo Leaderboard and A-B testing.\" |\n| [hate-functional-tests](https://github.com/paul-rottger/hate-functional-tests)![](https://img.shields.io/github/stars/paul-rottger/hate-functional-tests?style=social) | HateCheck: A dataset and test suite from an ACL 2021 paper, offering functional tests for hate speech detection models, including extensive case annotations and testing functionalities. |\n| [imodels](https://github.com/csinva/imodels)![](https://img.shields.io/github/stars/csinva/imodels?style=social) | \"Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.” |\n| [iNNvestigate neural nets](https://github.com/albermax/innvestigate)![](https://img.shields.io/github/stars/albermax/innvestigate?style=social) | A comprehensive Python library to analyze and interpret neural network behaviors in Keras, featuring a variety of methods like Gradient, LRP, and Deep Taylor. |\n| [Integrated-Gradients](https://github.com/ankurtaly/Integrated-Gradients)![](https://img.shields.io/github/stars/ankurtaly/Integrated-Gradients?style=social) | \"a variation on computing the gradient of the prediction output w.r.t. features of the input. It requires no modification to the original network, is simple to implement, and is applicable to a variety of deep models (sparse and dense, text and vision).” |\n| [interpret](https://github.com/interpretml/interpret)![](https://img.shields.io/github/stars/interpretml/interpret?style=social) | \"an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof.” |\n| [interpret_with_rules](https://github.com/clips/interpret_with_rules)![](https://img.shields.io/github/stars/clips/interpret_with_rules?style=social) | \"induces rules to explain the predictions of a trained neural network, and optionally also to explain the patterns that the model captures from the training data, and the patterns that are present in the original dataset.” |\n| [InterpretME](https://github.com/SDM-TIB/InterpretME)![](https://img.shields.io/github/stars/SDM-TIB/InterpretME?style=social) | \"integrates knowledge graphs (KG) with machine learning methods to generate interesting meaningful insights. It helps to generate human- and machine-readable decisions to provide assistance to users and enhance efficiency.” |\n| [Keras-vis](https://github.com/raghakot/keras-vis)![](https://img.shields.io/github/stars/raghakot/keras-vis?style=social) | \"a high-level toolkit for visualizing and debugging your trained keras neural net models.” |\n| [keract](https://github.com/philipperemy/keract/)![](https://img.shields.io/github/stars/philipperemy/keract?style=social) | Keract is a tool for visualizing activations and gradients in Keras models; it's meant to support a wide range of Tensorflow versions and to offer an intuitive API with Python examples. |\n| [L2X](https://github.com/Jianbo-Lab/L2X)![](https://img.shields.io/github/stars/Jianbo-Lab/L2X?style=social) | \"Code for replicating the experiments in the paper [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://arxiv.org/pdf/1802.07814.pdf) at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan.” |\n| [LangFair](https://github.com/cvs-health/langfair)![](https://img.shields.io/github/stars/cvs-health/langfair?style=social) | \"LangFair is a Python library for conducting use-case level LLM bias and fairness assessments\"\n| [langtest](https://github.com/JohnSnowLabs/langtest)![](https://img.shields.io/github/stars/JohnSnowLabs/langtest?style=social) | \"LangTest: Deliver Safe & Effective Language Models\" |\n| [learning-fair-representations](https://github.com/zjelveh/learning-fair-representations)![](https://img.shields.io/github/stars/zjelveh/learning-fair-representations?style=social) | \"Python numba implementation of Zemel et al. 2013 <http://www.cs.toronto.edu/~toni/Papers/icml-final.pdf>\" |\n| [leeky: Leakage/contamination testing for black box language models](https://github.com/mjbommar/leeky)![](https://img.shields.io/github/stars/mjbommar/leeky?style=social) | \"leeky - training data contamination techniques for blackbox models\" |\n| [leondz / garak, LLM vulnerability scanner](https://github.com/leondz/garak)![](https://img.shields.io/github/stars/leondz/garak?style=social) | \"LLM vulnerability scanner\" |\n| [lilac](https://github.com/lilacai/lilac)![](https://img.shields.io/github/stars/lilacai/lilac?style=social) | \"Curate better data for LLMs.\" |\n| [lime](https://github.com/marcotcr/lime)![](https://img.shields.io/github/stars/marcotcr/lime?style=social) | \"explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations).” |\n| [LiFT](https://github.com/linkedin/LiFT)![](https://img.shields.io/github/stars/linkedin/LiFT?style=social) | \"The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness and the mitigation of bias in large-scale machine learning workflows. The measurement module includes measuring biases in training data, evaluating fairness metrics for ML models, and detecting statistically significant differences in their performance across different subgroups.” |\n| [lit](https://github.com/pair-code/lit)![](https://img.shields.io/github/stars/pair-code/lit?style=social) | \"The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks.” |\n| [LLM Dataset Inference: Did you train on my dataset?](https://github.com/pratyushmaini/llm_dataset_inference/)![](https://img.shields.io/github/stars/pratyushmaini/llm_dataset_inference?style=social) | \"Official Repository for Dataset Inference for LLMs\" |\n| [lofo-importance](https://github.com/aerdem4/lofo-importance)![](https://img.shields.io/github/stars/aerdem4/lofo-importance?style=social) | \"LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.” |\n| [lrp_toolbox](https://github.com/sebastian-lapuschkin/lrp_toolbox)![](https://img.shields.io/github/stars/sebastian-lapuschkin/lrp_toolbox?style=social) | \"The Layer-wise Relevance Propagation (LRP) algorithm explains a classifer's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself.” |\n| [MindsDB](https://github.com/mindsdb/mindsdb)![](https://img.shields.io/github/stars/mindsdb/mindsdb?style=social) | \"enables developers to build AI tools that need access to real-time data to perform their tasks.” |\n| [MLextend](http://rasbt.github.io/mlxtend/) | \"Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.” |\n| [ml-fairness-gym](https://github.com/google/ml-fairness-gym)![](https://img.shields.io/github/stars/google/ml-fairness-gym?style=social) | \"a set of components for building simple simulations that explore the potential long-run impacts of deploying machine learning-based decision systems in social environments.” |\n| [ml_privacy_meter](https://github.com/privacytrustlab/ml_privacy_meter)![](https://img.shields.io/github/stars/privacytrustlab/ml_privacy_meter?style=social) | \"an open-source library to audit data privacy in statistical and machine learning algorithms. The tool can help in the data protection impact assessment process by providing a quantitative analysis of the fundamental privacy risks of a (machine learning) model.” |\n| [mllp](https://github.com/12wang3/mllp)![](https://img.shields.io/github/stars/12wang3/mllp?style=social) | \"This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: [Transparent Classification with Multilayer Logical Perceptrons and Random Binarization](https://arxiv.org/abs/1912.04695).” |\n| [Monotonic Constraints](http://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html) | Guide on implementing and understanding monotonic constraints in XGBoost models to enhance predictive performance with practical Python examples. |\n| [XGBoost](http://xgboost.readthedocs.io/en/latest/) | \"an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.” |\n| [Multilayer Logical Perceptron (MLLP)](https://github.com/12wang3/mllp)![](https://img.shields.io/github/stars/12wang3/mllp?style=social) | \"This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: [Transparent Classification with Multilayer Logical Perceptrons and Random Binarization](https://arxiv.org/abs/1912.04695).” |\n| [OptBinning](https://github.com/guillermo-navas-palencia/optbinning)![](https://img.shields.io/github/stars/guillermo-navas-palencia/optbinning?style=social) | \"a library written in Python implementing a rigorous and flexible mathematical programming formulation to solve the optimal binning problem for a binary, continuous and multiclass target type, incorporating constraints not previously addressed.” |\n| [Optimal Sparse Decision Trees](https://github.com/xiyanghu/OSDT)![](https://img.shields.io/github/stars/xiyanghu/OSDT?style=social) | \"This accompanies the paper, [\"Optimal Sparse Decision Trees\"](https://arxiv.org/abs/1904.12847) by Xiyang Hu, Cynthia Rudin, and Margo Seltzer.” |\n| [parity-fairness](https://pypi.org/project/parity-fairness/) | \"This repository contains codes that demonstrate the use of fairness metrics, bias mitigations and explainability tool.” |\n| [PDPbox](https://github.com/SauceCat/PDPbox)![](https://img.shields.io/github/stars/SauceCat/PDPbox?style=social) | \"Python Partial Dependence Plot toolbox. Visualize the influence of certain features on model predictions for supervised machine learning algorithms, utilizing partial dependence plots.” |\n| [PiML-Toolbox](https://github.com/SelfExplainML/PiML-Toolbox)![](https://img.shields.io/github/stars/SelfExplainML/PiML-Toolbox?style=social) | \"a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports a growing list of inherently interpretable ML models.” |\n| [pjsaelin / Cubist](https://github.com/pjaselin/Cubist?tab=readme-ov-file)![](https://img.shields.io/github/stars/pjaselin/Cubist?style=social) | \"A Python package for fitting Quinlan's Cubist regression model\" |\n| [Privacy-Preserving-ML](https://github.com/abhinav-bohra/Privacy-Preserving-ML)![](https://img.shields.io/github/stars/abhinav-bohra/Privacy-Preserving-ML?style=social) | \"Implementation of privacy-preserving SVM assuming public model private data scenario (data in encrypted but model parameters are unencrypted) using adequate partial homomorphic encryption.” |\n| [ProtoPNet](https://github.com/cfchen-duke/)![](https://img.shields.io/github/stars/cfchen-duke?style=social) | \"This code package implements the prototypical part network (ProtoPNet) from the paper \"This Looks Like That: Deep Learning for Interpretable Image Recognition\" (to appear at NeurIPS 2019), by Chaofan Chen (Duke University), Oscar Li| (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University).” |\n| [pyBreakDown](https://github.com/MI2DataLab/pyBreakDown)![](https://img.shields.io/github/stars/MI2DataLab/pyBreakDown?style=social) | See [dalex](https://dalex.drwhy.ai/). |\n| [PyCEbox](https://github.com/AustinRochford/PyCEbox)![](https://img.shields.io/github/stars/AustinRochford/PyCEbox?style=social) | \"Python Individual Conditional Expectation Plot Toolbox.” |\n| [pyGAM](https://github.com/dswah/pyGAM)![](https://img.shields.io/github/stars/dswah/pyGAM?style=social) | \"Generalized Additive Models in Python.” |\n| [pymc3](https://github.com/pymc-devs/pymc3)![](https://img.shields.io/github/stars/pymc-devs/pymc3?style=social) | \"PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.” |\n| [pySS3](https://github.com/sergioburdisso/pyss3)![](https://img.shields.io/github/stars/sergioburdisso/pyss3?style=social) | \"The SS3 text classifier is a novel and simple supervised machine learning model for text classification which is interpretable, that is, it has the ability to naturally (self)explain its rationale.” |\n| [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam)![](https://img.shields.io/github/stars/jacobgil/pytorch-grad-cam?style=social) | \"a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods.” |\n| [pytorch-innvestigate](https://github.com/fgxaos/pytorch-innvestigate)![](https://img.shields.io/github/stars/fgxaos/pytorch-innvestigate?style=social) | \"PyTorch implementation of Keras already existing project: [https://github.com/albermax/innvestigate/](https://github.com/albermax/innvestigate/).” |\n| [Quantus](https://github.com/understandable-machine-intelligence-lab/Quantus)![](https://img.shields.io/github/stars/understandable-machine-intelligence-lab/Quantus?style=social) | \"Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations.\" |\n| [rationale](https://github.com/taolei87/rcnn/tree/master/code/rationale)![](https://img.shields.io/github/stars/taolei87/rcnn?style=social) | \"This directory contains the code and resources of the following paper: *\"Rationalizing Neural Predictions\". Tao Lei, Regina Barzilay and Tommi Jaakkola. EMNLP 2016. [[PDF]](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf) [[Slides]](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale_slides.pdf)*. The method learns to provide justifications, i.e. rationales, as supporting evidence of neural networks' prediction.” |\n| [responsibly](https://github.com/ResponsiblyAI/responsibly)![](https://img.shields.io/github/stars/ResponsiblyAI/responsibly?style=social) | \"Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems.” |\n| [REVISE: REvealing VIsual biaSEs](https://github.com/princetonvisualai/revise-tool)![](https://img.shields.io/github/stars/princetonvisualai/revise-tool?style=social) | \"A tool that automatically detects possible forms of bias in a visual dataset along the axes of object-based, attribute-based, and geography-based patterns, and from which next steps for mitigation are suggested.” |\n| [robustness](https://github.com/MadryLab/robustness)![](https://img.shields.io/github/stars/MadryLab/robustness?style=social) | \"a package we (students in the [MadryLab](http://madry-lab.ml/)) created to make training, evaluating, and exploring neural networks flexible and easy.” |\n| [RISE](https://github.com/eclique/RISE)![](https://img.shields.io/github/stars/eclique/RISE?style=social) | \"contains source code necessary to reproduce some of the main results in the paper: [Vitali Petsiuk](http://cs-people.bu.edu/vpetsiuk/), [Abir Das](http://cs-people.bu.edu/dasabir/), [Kate Saenko](http://ai.bu.edu/ksaenko.html) (BMVC, 2018) [and] [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://arxiv.org/abs/1806.07421).” |\n| [Risk-SLIM](https://github.com/ustunb/risk-SLIM)![](https://img.shields.io/github/stars/ustunb/risk-SLIM?style=social) | \"a machine learning method to fit simple customized risk scores in python.” |\n| [SAGE](https://github.com/iancovert/sage/)![](https://img.shields.io/github/stars/iancovert/sage?style=social) | \"SAGE (Shapley Additive Global importancE) is a game-theoretic approach for understanding black-box machine learning models. It quantifies each feature's importance based on how much predictive power it contributes, and it accounts for complex feature interactions using the Shapley value.” |\n| [SALib](https://github.com/SALib/SALib)![](https://img.shields.io/github/stars/SALib/SALib?style=social) | \"Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.” |\n| [Scikit-Explain](https://scikit-explain.readthedocs.io/en/latest/index.html) | \"User-friendly Python module for machine learning explainability,\" featuring PD and ALE plots, LIME, SHAP, permutation importance and Friedman's H, among other methods. |\n| [Scikit-learn](https://scikit-learn.org/stable/) [Decision Trees](http://scikit-learn.org/stable/modules/tree.html) | \"a non-parametric supervised learning method used for classification and regression.” |\n| [Scikit-learn](https://scikit-learn.org/stable/) [Generalized Linear Models](http://scikit-learn.org/stable/modules/linear_model.html) | \"a set of methods intended for regression in which the target value is expected to be a linear combination of the features.” |\n| [Scikit-learn](https://scikit-learn.org/stable/) [Sparse Principal Components](http://scikit-learn.org/stable/modules/decomposition.html#sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca) | \"a variant of [principal component analysis, PCA], with the goal of extracting the set of sparse components that best reconstruct the data.” |\n| [scikit-fairness](https://github.com/koaning/scikit-fairness)![](https://img.shields.io/github/stars/koaning/scikit-fairness?style=social) | Historical link. Merged with [fairlearn](https://fairlearn.org/). |\n| [scikit-multiflow](https://scikit-multiflow.github.io/) | \"a machine learning package for streaming data in Python.” |\n| [shap](https://github.com/slundberg/shap)![](https://img.shields.io/github/stars/slundberg/shap?style=social) | \"a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions\"\n| [shapley](https://github.com/benedekrozemberczki/shapley)![](https://img.shields.io/github/stars/benedekrozemberczki/shapley?style=social) | \"a Python library for evaluating binary classifiers in a machine learning ensemble.” |\n| [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys)![](https://img.shields.io/github/stars/tmadl/sklearn-expertsys?style=social) | \"a scikit-learn compatible wrapper for the Bayesian Rule List classifier developed by Letham et al., 2015, extended by a minimum description length-based discretizer (Fayyad & Irani, 1993) for continuous data, and by an approach to subsample large datasets for better performance.” |\n| [skope-rules](https://github.com/scikit-learn-contrib/skope-rules)![](https://img.shields.io/github/stars/scikit-learn-contrib/skope-rules?style=social) | \"a Python machine learning module built on top of scikit-learn and distributed under the 3-Clause BSD license.” |\n| [solas-ai-disparity](https://github.com/SolasAI/solas-ai-disparity)![](https://img.shields.io/github/stars/SolasAI/solas-ai-disparity?style=social) | \"a collection of tools that allows modelers, compliance, and business stakeholders to test outcomes for bias or discrimination using widely accepted fairness metrics.” |\n| [Super-sparse Linear Integer models (SLIMs)](https://github.com/ustunb/slim-python)![](https://img.shields.io/github/stars/ustunb/slim-python?style=social) | \"a package to learn customized scoring systems for decision-making problems.” |\n| [tensorflow/lattice](https://github.com/tensorflow/lattice)![](https://img.shields.io/github/stars/tensorflow/lattice?style=social) | \"a library that implements constrained and interpretable lattice based models. It is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow.” |\n| [tensorflow/lucid](https://github.com/tensorflow/lucid)![](https://img.shields.io/github/stars/tensorflow/lucid?style=social) | \"a collection of infrastructure and tools for research in neural network interpretability.” |\n| [tensorflow/fairness-indicators](https://github.com/tensorflow/fairness-indicators)![](https://img.shields.io/github/stars/tensorflow/fairness-indicators?style=social) | \"designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader Tensorflow toolkit.” |\n| [tensorflow/model-analysis](https://github.com/tensorflow/model-analysis)![](https://img.shields.io/github/stars/tensorflow/model-analysis?style=social) | \"a library for evaluating TensorFlow models. It allows users to evaluate their models on large amounts of data in a distributed manner, using the same metrics defined in their trainer. These metrics can be computed over different slices of data and visualized in Jupyter notebooks.” |\n| [tensorflow/model-card-toolkit](https://github.com/tensorflow/model-card-toolkit)![](https://img.shields.io/github/stars/tensorflow/model-card-toolkit?style=social) | \"streamlines and automates generation of Model Cards, machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables you to share model metadata and metrics with researchers, developers, reporters, and more.” |\n| [tensorflow/model-remediation](https://github.com/tensorflow/model-remediation)![](https://img.shields.io/github/stars/tensorflow/model-remediation?style=social) | \"a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.” |\n| [tensorflow/privacy](https://github.com/tensorflow/privacy)![](https://img.shields.io/github/stars/tensorflow/privacy?style=social) | \"the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided.” |\n| [tensorflow/tcav](https://github.com/tensorflow/tcav)![](https://img.shields.io/github/stars/tensorflow/tcav?style=social) | \"Testing with Concept Activation Vectors (TCAV) is a new interpretability method to understand what signals your neural networks models uses for prediction.” |\n| [tensorfuzz](https://github.com/brain-research/tensorfuzz)![](https://img.shields.io/github/stars/brain-research/tensorfuzz?style=social) | \"a library for performing coverage guided fuzzing of neural networks.” |\n| [TensorWatch](https://github.com/microsoft/tensorwatch)![](https://img.shields.io/github/stars/microsoft/tensorwatch?style=social) | \"a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.” |\n| [TextFooler](https://github.com/jind11/TextFooler)![](https://img.shields.io/github/stars/jind11/TextFooler?style=social) | \"A Model for Natural Language Attack on Text Classification and Inference\"\n| [text_explainability](https://text-explainability.readthedocs.io/) | \"text_explainability provides a generic architecture from which well-known state-of-the-art explainability approaches for text can be composed.” |\n| [text_sensitivity](https://text-sensitivity.readthedocs.io/) | \"Uses the generic architecture of text_explainability to also include tests of safety (how safe it the model in production, i.e. types of inputs it can handle), robustness (how generalizable the model is in production, e.g. stability when adding typos, or the effect of adding random unrelated data) and fairness (if equal individuals are treated equally by the model, e.g. subgroup fairness on sex and nationality).” |\n| [tf-explain](https://github.com/sicara/tf-explain)![](https://img.shields.io/github/stars/sicara/tf-explain?style=social) | \"Implements interpretability methods as Tensorflow 2.x callbacks to ease neural network's understanding.” |\n| [Themis](https://github.com/LASER-UMASS/Themis)![](https://img.shields.io/github/stars/LASER-UMASS/Themis?style=social) | \"A testing-based approach for measuring discrimination in a software system.” |\n| [themis-ml](https://github.com/cosmicBboy/themis-ml)![](https://img.shields.io/github/stars/cosmicBboy/themis-ml?style=social) | \"A Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms.” |\n| [TorchUncertainty](https://github.com/ENSTA-U2IS/torch-uncertainty)![](https://img.shields.io/github/stars/ENSTA-U2IS/torch-uncertainty?style=social) | \"A package designed to help you leverage uncertainty quantification techniques and make your deep neural networks more reliable.” |\n| [treeinterpreter](https://github.com/andosa/treeinterpreter)![](https://img.shields.io/github/stars/andosa/treeinterpreter?style=social) | \"Package for interpreting scikit-learn's decision tree and random forest predictions.” |\n| [TRIAGE](https://github.com/seedatnabeel/TRIAGE)![](https://img.shields.io/github/stars/seedatnabeel/TRIAGE?style=social) | \"This repository contains the implementation of TRIAGE, a \"Data-Centric AI\" framework for data characterization tailored for regression.” |\n| [woe](https://github.com/boredbird/woe)![](https://img.shields.io/github/stars/boredbird/woe?style=social) | \"Tools for WoE Transformation mostly used in ScoreCard Model for credit rating.” |\n| [xai](https://github.com/EthicalML/xai)![](https://img.shields.io/github/stars/EthicalML/xai?style=social) | \"A Machine Learning library that is designed with AI explainability in its core.” |\n| [xdeep](https://github.com/datamllab/xdeep)![](https://img.shields.io/github/stars/datamllab/xdeep?style=social) | \"An open source Python library for Interpretable Machine Learning.” |\n| [xplique](https://github.com/deel-ai/xplique)![](https://img.shields.io/github/stars/deel-ai/xplique?style=social) | \"A Python toolkit dedicated to explainability. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models.” |\n| [ydata-profiling](https://github.com/ydataai/ydata-profiling)![](https://img.shields.io/github/stars/ydataai/ydata-profiling?style=social) | \"Provide[s] a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution.” |\n| [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick)![](https://img.shields.io/github/stars/DistrictDataLabs/yellowbrick?style=social) | \"A suite of visual diagnostic tools called \"Visualizers\" that extend the scikit-learn API to allow human steering of the model selection process.” |\n \n#### R\n\n| Name | Description |\n|------|-------------|\n| [ALEPlot](https://cran.r-project.org/web/packages/ALEPlot/index.html) | \"Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models.\"  |\n| [arules](https://cran.r-project.org/web/packages/arules/index.html) | \"Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005).\" |\n| [Causal SVM](https://github.com/shangtai/githubcausalsvm)![](https://img.shields.io/github/stars/shangtai/githubcausalsvm?style=social) | \"We present a new machine learning approach to estimate whether a treatment has an effect on an individual, in the setting of the classical potential outcomes framework with binary outcomes.\" |\n| [DALEX](https://github.com/ModelOriented/DALEX)![](https://img.shields.io/github/stars/ModelOriented/DALEX?style=social) | \"moDel Agnostic Language for Exploration and eXplanation.\" |\n| [DALEXtra: Extension for 'DALEX' Package](https://cran.r-project.org/web/packages/DALEXtra/index.html) | \"Provides wrapper of various machine learning models.\" |\n| [DrWhyAI](https://github.com/ModelOriented/DrWhy)![](https://img.shields.io/github/stars/ModelOriented/DrWhy?style=social) | \"DrWhy is [a] collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.\" |\n| [elasticnet](https://cran.r-project.org/web/packages/elasticnet/index.html) | \"Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for doing sparse PCA.\" |\n| [ExplainPrediction](https://github.com/rmarko/ExplainPrediction)![](https://img.shields.io/github/stars/rmarko/ExplainPrediction?style=social) | \"Generates explanations for classification and regression models and visualizes them.\" |\n| [Explainable Boosting Machine (EBM)/GA2M](https://cran.r-project.org/web/packages/interpret/index.html) | \"Package for training interpretable machine learning models.\" |\n| [fairmodels](https://github.com/ModelOriented/fairmodels)![](https://img.shields.io/github/stars/ModelOriented/fairmodels?style=social) | \"Flexible tool for bias detection, visualization, and mitigation. Use models explained with DALEX and calculate fairness classification metrics based on confusion matrices using fairness_check() or try newly developed module for regression models using fairness_check_regression().\" |\n| [fairness](https://cran.r-project.org/web/packages/fairness/index.html) | \"Offers calculation, visualization and comparison of algorithmic fairness metrics.\" |\n| [fastshap](https://github.com/bgreenwell/fastshap)![](https://img.shields.io/github/stars/bgreenwell/fastshap?style=social) | \"The goal of fastshap is to provide an efficient and speedy approach (at least relative to other implementations) for computing approximate Shapley values, which help explain the predictions from any machine learning model.\" |\n| [featureImportance](https://github.com/giuseppec/featureImportance)![](https://img.shields.io/github/stars/giuseppec/featureImportance?style=social) | \"An extension for the mlr package and allows to compute the permutation feature importance in a model-agnostic manner.\" |\n| [flashlight](https://github.com/mayer79/flashlight)![](https://img.shields.io/github/stars/mayer79/flashlight?style=social) | \"The goal of this package is [to] shed light on black box machine learning models.\" |\n| [forestmodel](https://cran.r-project.org/web/packages/forestmodel/index.html) | \"Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph().\" |\n| [fscaret](https://cran.r-project.org/web/packages/fscaret/) | \"Automated feature selection using variety of models provided by 'caret' package.\" |\n| [gam](https://cran.r-project.org/web/packages/gam/index.html) | \"Functions for fitting and working with generalized additive models, as described in chapter 7 of \"Statistical Models in S\" (Chambers and Hastie (eds), 1991), and \"Generalized Additive Models\" (Hastie and Tibshirani, 1990).\" |\n| [glm2](https://cran.r-project.org/web/packages/glm2/) | \"Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.\" |\n| [glmnet](https://cran.r-project.org/web/packages/glmnet/index.html) | \"Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression.\" |\n| [H2O-3](https://github.com/h2oai/h2o-3) [Penalized Generalized Linear Models](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.glm.html) | \"Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.\" |\n| [H2O-3](https://github.com/h2oai/h2o-3) [Monotonic GBM](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.gbm.html) | \"Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data set.\" |\n| [H2O-3](https://github.com/h2oai/h2o-3) [Sparse Principal Components (GLRM)](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.glrm.html) | \"Builds a generalized low rank decomposition of an H2O data frame.\" |\n| [iBreakDown](https://github.com/ModelOriented/iBreakDown)![](https://img.shields.io/github/stars/ModelOriented/iBreakDown?style=social) | \"A model agnostic tool for explanation of predictions from black boxes ML models.\"|\n| [ICEbox: Individual Conditional Expectation Plot Toolbox](https://cran.r-project.org/web/packages/ICEbox/index.html) | \"Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm.\"|\n| [iml](https://github.com/christophM/iml)![](https://img.shields.io/github/stars/christophM/iml?style=social) | \"An R package that interprets the behavior and explains predictions of machine learning models.\"|\n| [ingredients](https://github.com/ModelOriented/ingredients)![](https://img.shields.io/github/stars/ModelOriented/ingredients?style=social) | \"A collection of tools for assessment of feature importance and feature effects.\"|\n| [interpret: Fit Interpretable Machine Learning Models](https://cran.r-project.org/web/packages/interpret/index.html) | \"Package for training interpretable machine learning models.\"|\n| [lightgbmExplainer](https://github.com/lantanacamara/lightgbmExplainer)![](https://img.shields.io/github/stars/lantanacamara/lightgbmExplainer?style=social) | \"An R package that makes LightGBM models fully interpretable.\"|\n| [lime](https://github.com/thomasp85/lime)![](https://img.shields.io/github/stars/thomasp85/lime?style=social) | \"R port of the Python lime package.\"|\n| [live](https://cran.r-project.org/web/packages/live/index.html) | \"Helps to understand key factors that drive the decision made by complicated predictive model (black box model).\"|\n| [mcr](https://github.com/aaronjfisher/mcr)![](https://img.shields.io/github/stars/aaronjfisher/mcr?style=social) | \"An R package for Model Reliance and Model Class Reliance.\"|\n| [modelDown](https://cran.r-project.org/web/packages/modelDown/index.html) | \"Website generator with HTML summaries for predictive models.\"|\n| [modelOriented](https://github.com/ModelOriented)![](https://img.shields.io/github/stars/ModelOriented?style=social) | GitHub repositories of Warsaw-based MI².AI. |\n| [modelStudio](https://github.com/ModelOriented/modelStudio)![](https://img.shields.io/github/stars/ModelOriented/modelStudio?style=social) | \"Automates the explanatory analysis of machine learning predictive models.\"|\n| [Monotonic](http://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html) [XGBoost](http://xgboost.readthedocs.io/en/latest/) | Enforces consistent, directional relationships between features and predicted outcomes, enhancing model performance by aligning with prior data expectations. |\n| [quantreg](https://cran.r-project.org/web/packages/quantreg/index.html) | \"Estimation and inference methods for models for conditional quantile functions.\" |\n| [rpart](https://cran.r-project.org/web/packages/rpart/index.html) | \"Recursive partitioning for classification, regression and survival trees.\" |\n| [RuleFit](http://statweb.stanford.edu/~jhf/R_RuleFit.html) | \"Implements the learning method and interpretational tools described in *Predictive Learning via Rule Ensembles*.\" |\n| [Scalable Bayesian Rule Lists (SBRL)](https://users.cs.duke.edu/~cynthia/code/sbrl_1.0.tar.gz) | A more scalable implementation of Bayesian rule list from the Rudin group at Duke. |\n| [shapFlex](https://github.com/nredell/shapFlex)![](https://img.shields.io/github/stars/nredell/shapFlex?style=social) | Computes stochastic Shapley values for machine learning models to interpret them and evaluate fairness, including causal constraints in the feature space. |\n| [shapleyR](https://github.com/redichh/ShapleyR)![](https://img.shields.io/github/stars/redichh/ShapleyR?style=social) | \"An R package that provides some functionality to use mlr tasks and models to generate shapley values.\" |\n| [shapper](https://cran.r-project.org/web/packages/shapper/index.html) | \"Provides SHAP explanations of machine learning models.\" |\n| [smbinning](https://cran.r-project.org/web/packages/smbinning/index.html) | \"A set of functions to build a scoring model from beginning to end.\" |\n| [vip](https://github.com/koalaverse/vip)![](https://img.shields.io/github/stars/koalaverse/vip?style=social) | \"An R package for constructing variable importance plots (VIPs).\" |\n| [xgboostExplainer](https://github.com/AppliedDataSciencePartners/xgboostExplainer)![](https://img.shields.io/github/stars/AppliedDataSciencePartners/xgboostExplainer?style=social) | \"An R package that makes xgboost models fully interpretable. |\n\n### Archived\n#### Archived: Official Policy, Frameworks, and Guidance\nFor official government files pertaining to responsible AI practices that have been taken offline, we provide Wayback Machine mirror links below. If a document is still available on its original official domain, it can currently be found in its respective subsection above, although it may later be incorporated into this list. Documents may be removed for various reasons (whether political or through routine updates), but archiving them ensures they remain accessible for historical reference. If you're a researcher who finds a dead link to an older version of a government document or one that has altogether been deleted without comment, please feel free to submit a pull request drawing our attention to it and we'll consider it for inclusion. Where possible, we provide links to what appear to be the most recent URLs that governments may want the public to access.\n  \n* Australia, Office of the National Data Commissioner, April 1, 2022, archived March 14, 2024 | [Data Availability and Transparency Act 2022](https://web.archive.org/web/20240314232025/https://www.datacommissioner.gov.au/law/dat-act)\n  * [Introducing the DATA Scheme](https://www.datacommissioner.gov.au/the-data-scheme)\n  * [Federal Register of Legislation, Data Availability and Transparency Act 2022](https://www.legislation.gov.au/C2022A00011/latest/text)\n* Canada, Office of the Superintendent of Financial Institutions of Canada, September 2020, archived August 2, 2023 | [Developing Financial Sector Resilience in a Digital World: Selected Themes in Technology and Related Risks](https://web.archive.org/web/20230802000841/https://www.osfi-bsif.gc.ca/Eng/Docs/tchrsk.pdf)\n  * [Same or slightly revised document but different URL](https://www.osfi-bsif.gc.ca/sites/default/files/documents/tchrsk_EN.pdf)\n* Philippines, National Privacy Commission, December 19, 2024, archived January 12, 2025 | [Guidelines on the Application of Republic Act No. 10173 or the Data Privacy Act of 2012 DPA, Its Implementing Rules and Regulations, and the Issuances of the Commission to Artificial Intelligence Systems Processing Personal Data NPC Advisory No. 2024-04](https://web.archive.org/web/20250112215325/https://privacy.gov.ph/wp-content/uploads/2024/12/Advisory-2024.12.19-Guidelines-on-Artificial-Intelligence-w-SGD.pdf)\n  * [Same or slightly revised document but different URL](https://privacy.gov.ph/wp-content/uploads/2025/02/Advisory-2024.12.19-Guidelines-on-Artificial-Intelligence-w-SGD.pdf)\n* State of California, Department of Technology, Office of Information Security, March 2024, archived May 24, 2024 | [Generative Artificial Intelligence Risk Assessment SIMM 5305-F](https://web.archive.org/web/20240524154534/https://cdt.ca.gov/wp-content/uploads/2024/03/SIMM-5305-F-Generative-Artificial-Intelligence-Risk-Assessment-FINAL.pdf)\n  * [February 2025 update](https://cdt.ca.gov/wp-content/uploads/2025/01/SIMM-5305-F-GenAI-Risk-Assessment-2025_0131-final.pdf)\n* United States, Department of Defense, Chief Digital and Artificial Intelligence Office (CDAO), archived September 26, 2024 | [Generative Artificial Intelligence Lexicon](https://web.archive.org/web/20240926203350/https://www.ai.mil/lexicon_ai_terms.html)\n* United States, Department of Labor, archived February 5, 2025 | [Artificial Intelligence and Worker Well-Being: Principles and Best Practices for Developers and Employers](https://web.archive.org/web/20250205182942/https://www.dol.gov/sites/dolgov/files/general/ai/AI-Principles-Best-Practices.pdf)\n* United States, Executive Office of the President, National Science and Technology Council, Select Committee on Artificial Intelligence, May 2023, archived January 16, 2025 | [National Artificial Intelligence Research and Development Strategic Plan 2023 Update](https://web.archive.org/web/20250116083052/https://www.whitehouse.gov/wp-content/uploads/2023/05/National-Artificial-Intelligence-Research-and-Development-Strategic-Plan-2023-Update.pdf)\n* United States, Executive Office of the President, Office of Management and Budget, November 17, 2020, archived January 18, 2025 | [M-21-06 Memorandum for the Heads of Executive Departments and Agencies, Guidance for Regulation of Artificial Intelligence Applications](https://web.archive.org/web/20250118013159/https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf)\n* United States, Executive Office of the President, Office of Management and Budget, September 24, 2024, archived January 18, 2025 | [M-24-18 Memorandum for the Heads of Executive Departments and Agencies, Advancing the Responsible Acquisition of Artificial Intelligence in Government](https://web.archive.org/web/20250118023352/https://www.whitehouse.gov/wp-content/uploads/2024/10/M-24-18-AI-Acquisition-Memorandum.pdf)\n* United States, Federal Deposit Insurance Corporation, archived February 13, 2024 | [Supervisory Guidance on Model Risk Management](https://www.fdic.gov/news/financial-institution-letters/2017/fil17022a.pdf)\n* United States, Federal Trade Commission, Elisa Jillson, April 19, 2021, archived January 17, 2025 | [Aiming for truth, fairness, and equity in your company’s use of AI](https://web.archive.org/web/20250117235232/https://www.ftc.gov/business-guidance/blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai)\n* United States, Federal Trade Commission, Andrew Smith, April 8, 2020, archived January 15, 2024 | [Using Artificial Intelligence and Algorithms](https://web.archive.org/web/20240115210007/https://www.ftc.gov/business-guidance/blog/2020/04/using-artificial-intelligence-and-algorithms)\n* United States, The White House, Office of Science and Technology Policy, January 13, 2021, archived January 20, 2025 | [Office of Science and Technology Policy](https://web.archive.org/web/20250120110259/https://www.whitehouse.gov/ostp/)\n* United States, The White House, Office of Science and Technology Policy, January 16, 2021, archived January 18, 2025 | [National Science and Technology Council](https://web.archive.org/web/20250118020849/https://www.whitehouse.gov/ostp/ostps-teams/nstc/) \n* United States, The White House, Office of Science and Technology Policy, October 4, 2022, archived January 20, 2025 | [Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, HTML](https://web.archive.org/web/20250119213350/https://www.whitehouse.gov/ostp/ai-bill-of-rights/)\n*  United States, The White House, Office of Science and Technology Policy, October 4, 2022, archived January 20, 2025 | [Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, PDF](https://web.archive.org/web/20250119213350/https://www.whitehouse.gov/ostp/ai-bill-of-rights/)\n* United States, The White House, May 23, 2023, archived January 17, 2025 | [FACT SHEET: Biden-⁠Harris Administration Takes New Steps to Advance Responsible Artificial Intelligence Research, Development, and Deployment](https://web.archive.org/web/20250117044009/https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/23/fact-sheet-biden-harris-administration-takes-new-steps-to-advance-responsible-artificial-intelligence-research-development-and-deployment/)\n* United States, The White House, July 21, 2023, archived January 20, 2025 | [FACT SHEET: Biden-⁠Harris Administration Secures Voluntary Commitments from Leading Artificial Intelligence Companies to Manage the Risks Posed by AI](https://web.archive.org/web/20250120131235/https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/)\n* United States, The White House, October 30, 2023, archived January 20, 2025 | [Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence](https://web.archive.org/web/20250120132537/https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/)\n* United States, The White House, October 30, 2023, archived January 18, 2025 | [FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence](https://web.archive.org/web/20250118214923/https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/)\n* United States, The White House, July 26, 2024, archived January 20, 2025 | [FACT SHEET: Biden-⁠Harris Administration Announces New AI Actions and Receives Additional Major Voluntary Commitment on AI](https://web.archive.org/web/20250120101805/https://www.whitehouse.gov/briefing-room/statements-releases/2024/07/26/fact-sheet-biden-harris-administration-announces-new-ai-actions-and-receives-additional-major-voluntary-commitment-on-ai/)\n* United States, The White House, October 24, 2024, archived January 19, 2025 | [FACT SHEET: Biden-⁠Harris Administration Outlines Coordinated Approach to Harness Power of AI for U.S. National Security](https://web.archive.org/web/20250119050242/https://www.whitehouse.gov/briefing-room/statements-releases/2024/10/24/fact-sheet-biden-harris-administration-outlines-coordinated-approach-to-harness-power-of-ai-for-u-s-national-security/)\n* United States, The White House, October 24, 2024, archived January 16, 2025 | [Memorandum on Advancing the United States’ Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence](https://web.archive.org/web/20250116072308/https://www.whitehouse.gov/briefing-room/presidential-actions/2024/10/24/memorandum-on-advancing-the-united-states-leadership-in-artificial-intelligence-harnessing-artificial-intelligence-to-fulfill-national-security-objectives-and-fostering-the-safety-security/)\n\n### Citing Awesome Machine Learning Interpretability\n\nContributors with over 100 edits can be named coauthors in the citation of visible names. Otherwise, all contributors with fewer than 100 edits are included under \"et al.\"\n\n#### Bibtex\n\n```\n@misc{amli_repo,\n  author={Patrick Hall and Daniel Atherton},\n  title={Awesome Machine Learning Interpretability},\n  year={2024},\n  note={\\url{https://github.com/jphall663/awesome-machine-learning-interpretability}}\n}\n```\n\n#### ACM, APA, Chicago, and MLA\n\n* **ACM (Association for Computing Machinery)**\n\nHall, Patrick, Daniel Atherton, et al. 2024. Awesome Machine Learning Interpretability. GitHub. https://github.com/jphall663/awesome-machine-learning-interpretability.\n\n* **APA (American Psychological Association) 7th Edition**\n\nHall, Patrick, Daniel Atherton, et al. (2024). Awesome Machine Learning Interpretability [GitHub repository]. GitHub. https://github.com/jphall663/awesome-machine-learning-interpretability.\n\n* **Chicago Manual of Style 17th Edition**\n\nHall, Patrick, Daniel Atherton, et al. \"Awesome Machine Learning Interpretability.\" GitHub. Last modified 2023. https://github.com/jphall663/awesome-machine-learning-interpretability.\n\n* **MLA (Modern Language Association) 9th Edition**\n\nHall, Patrick, Daniel Atherton, et al. \"Awesome Machine Learning Interpretability.\" *GitHub*, 2024, https://github.com/jphall663/awesome-machine-learning-interpretability. Accessed 5 March 2024.\n"
  },
  {
    "path": "archive/README_2023_09.md.bak",
    "content": "# awesome-machine-learning-*interpretability* [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA curated, but probably biased and incomplete, list of awesome machine learning interpretability resources.\n\nIf you want to contribute to this list (*and please do!*) read over the [contribution guidelines](contributing.md), send a pull request, or contact me [@jpatrickhall](https://twitter.com/jpatrickhall).\n\n**An incomplete, imperfect blueprint for a more human-centered, lower-risk machine learning.** The resources in this repository can be used to do many of these things today. *The resources in this repository should not be considered legal compliance advice.*\n![alt-text](https://github.com/h2oai/mli-resources/blob/master/blueprint.png)\n</br>Image credit: H2O.ai Machine Learning Interpretability team, https://github.com/h2oai/mli-resources.\n\n\n## Table of Contents\n\n* [Comprehensive Software Examples and Tutorials](https://github.com/jphall663/awesome-machine-learning-interpretability#comprehensive-software-examples-and-tutorials)\n* Explainability- or Fairness-Enhancing Software Packages\n  * [Browser](https://github.com/jphall663/awesome-machine-learning-interpretability#browser)\n  * [Python](https://github.com/jphall663/awesome-machine-learning-interpretability#python)\n  * [R](https://github.com/jphall663/awesome-machine-learning-interpretability#r)\n* [Machine learning environment management tools](https://github.com/jphall663/awesome-machine-learning-interpretability#machine-learning-environment-management-tools)\n* [Free Books](https://github.com/jphall663/awesome-machine-learning-interpretability#free-books)\n* [Government and Regulatory Documents](https://github.com/jphall663/awesome-machine-learning-interpretability#government-and-regulatory-documents)\n* [Other Interpretability and Fairness Resources and Lists](https://github.com/jphall663/awesome-machine-learning-interpretability#other-interpretability-and-fairness-resources-and-lists)\n* [Review and General Papers](https://github.com/jphall663/awesome-machine-learning-interpretability#review-and-general-papers)\n* [Classes](https://github.com/jphall663/awesome-machine-learning-interpretability#classes)\n* Interpretable (\"Whitebox\") or Fair Modeling Packages\n  * [C/C++](https://github.com/jphall663/awesome-machine-learning-interpretability#cc)\n  * [Python](https://github.com/jphall663/awesome-machine-learning-interpretability#python-1)\n  * [R](https://github.com/jphall663/awesome-machine-learning-interpretability#r-1)\n* [AI Incident Tracker](https://github.com/jphall663/awesome-machine-learning-interpretability/blob/master/README.md#ai-incident-tracker)\n\n## Comprehensive Software Examples and Tutorials\n\n* [COMPAS Analysis Using Aequitas](https://github.com/dssg/aequitas/blob/master/docs/source/examples/compas_demo.ipynb)\n* [Explaining Quantitative Measures of Fairness (with SHAP)](https://github.com/slundberg/shap/blob/master/notebooks/overviews/Explaining%20quantitative%20measures%20of%20fairness.ipynb)\n* [Getting a Window into your Black Box Model](http://projects.rajivshah.com/inter/ReasonCode_NFL.html)\n* [From GLM to GBM Part 1](https://www.h2o.ai/blog/from-glm-to-gbm-part-1/)\n* [From GLM to GBM Part 2](https://www.h2o.ai/blog/from-glm-to-gbm-part-2/)\n* [IML](https://mybinder.org/v2/gh/christophM/iml/master?filepath=./notebooks/tutorial-intro.ipynb)\n* [Interpretable Machine Learning with Python](https://github.com/jphall663/interpretable_machine_learning_with_python)\n* [Interpreting Machine Learning Models with the iml Package](http://uc-r.github.io/iml-pkg)\n* [Interpretable Machine Learning using Counterfactuals](https://docs.seldon.io/projects/alibi/en/v0.2.0/examples/cf_mnist.html)\n* [Machine Learning Explainability by Kaggle Learn](https://www.kaggle.com/learn/machine-learning-explainability)\n* [Model Interpretability with DALEX](http://uc-r.github.io/dalex)\n* Model Interpretation series by Dipanjan (DJ) Sarkar:\n  * [The Importance of Human Interpretable Machine Learning](https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476)\n  * [Model Interpretation Strategies](https://towardsdatascience.com/explainable-artificial-intelligence-part-2-model-interpretation-strategies-75d4afa6b739)\n  * [Hands-on Machine Learning Model Interpretation](https://towardsdatascience.com/explainable-artificial-intelligence-part-3-hands-on-machine-learning-model-interpretation-e8ebe5afc608)\n  * [Interpreting Deep Learning Models for Computer Vision](https://towardsdatascience.com/explainable-artificial-intelligence-part-3-hands-on-machine-learning-model-interpretation-e8ebe5afc608)\n* [Partial Dependence Plots in R](https://journal.r-project.org/archive/2017/RJ-2017-016/)\n* [Saliency Maps for Deep Learning](https://medium.com/@thelastalias/saliency-maps-for-deep-learning-part-1-vanilla-gradient-1d0665de3284)\n* [Visualizing ML Models with LIME](http://uc-r.github.io/lime)\n* [Visualizing and debugging deep convolutional networks](https://rohitghosh.github.io/2018/01/05/visualising-debugging-deep-neural-networks/)\n* [What does a CNN see?](https://colab.research.google.com/drive/1xM6UZ9OdpGDnHBljZ0RglHV_kBrZ4e-9)\n\n## Explainability- or Fairness-Enhancing Software Packages\n\n### Browser\n\n* [DiscriLens](https://github.com/wangqianwen0418/DiscriLens)\n* [manifold](https://github.com/uber/manifold)\n* [TensorBoard Projector](http://projector.tensorflow.org)\n* [What-if Tool](https://pair-code.github.io/what-if-tool/index.html#about)\n\n### Python\n\n* [acd](https://github.com/csinva/hierarchical_dnn_interpretations)\n* [aequitas](https://github.com/dssg/aequitas)\n* [AI Fairness 360](http://aif360.mybluemix.net)\n* [AI Explainability 360](https://github.com/IBM/AIX360)\n* [ALEPython](https://github.com/blent-ai/ALEPython)\n* [Aletheia](https://github.com/SelfExplainML/Aletheia)\n* [allennlp](https://github.com/allenai/allennlp)\n* [algofairness](https://github.com/algofairness)\n* [Alibi](https://github.com/SeldonIO/alibi)\n* [anchor](https://github.com/marcotcr/anchor)\n* [BlackBoxAuditing](https://github.com/algofairness/BlackBoxAuditing)\n* [casme](https://github.com/kondiz/casme)\n* [Causal Discovery Toolbox](https://github.com/FenTechSolutions/CausalDiscoveryToolbox)\n* [captum](https://github.com/pytorch/captum)\n* [causalml](https://github.com/uber/causalml)\n* [cdt15](https://github.com/cdt15)\n* [checklist](https://github.com/marcotcr/checklist)\n* [contextual-AI](https://github.com/SAP/contextual-ai)\n* [ContrastiveExplanation (Foil Trees)](https://github.com/MarcelRobeer/ContrastiveExplanation)\n* [counterfit](https://github.com/Azure/counterfit/)\n* [dalex](https://github.com/ModelOriented/DALEX)\n* [debiaswe](https://github.com/tolga-b/debiaswe)\n* [DeepExplain](https://github.com/marcoancona/DeepExplain)\n* [deeplift](https://github.com/kundajelab/deeplift)\n* [deepvis](https://github.com/yosinski/deep-visualization-toolbox)\n* [DiCE](https://github.com/interpretml/DiCE)\n* [DoWhy](https://github.com/microsoft/dowhy)\n* [ecco](https://github.com/jalammar/ecco)\n* [eli5](https://github.com/TeamHG-Memex/eli5)\n* [explainerdashboard](https://github.com/oegedijk/explainerdashboard)\n* [fairml](https://github.com/adebayoj/fairml)\n* [fairlearn](https://github.com/fairlearn/fairlearn)\n* [fairness-comparison](https://github.com/algofairness/fairness-comparison)\n* [fairness_measures_code](https://github.com/megantosh/fairness_measures_code)\n* [foolbox](https://github.com/bethgelab/foolbox)\n* [Grad-CAM](https://github.com/topics/grad-cam) (GitHub topic)\n* [gplearn](https://github.com/trevorstephens/gplearn)\n* [hate-functional-tests](https://github.com/paul-rottger/hate-functional-tests)\n* [imodels](https://github.com/csinva/imodels)\n* [iNNvestigate neural nets](https://github.com/albermax/innvestigate)\n* [Integrated-Gradients](https://github.com/ankurtaly/Integrated-Gradients)\n* [interpret](https://github.com/interpretml/interpret)\n* [interpret_with_rules](https://github.com/clips/interpret_with_rules)\n* [imodels](https://github.com/csinva/imodels)\n* [Keras-vis](https://github.com/raghakot/keras-vis)\n* [keract](https://github.com/philipperemy/keract/)\n* [L2X](https://github.com/Jianbo-Lab/L2X)\n* [lime](https://github.com/marcotcr/lime)\n* [LiFT](https://github.com/linkedin/LiFT)\n* [lit](https://github.com/pair-code/lit)\n* [lofo-importance](https://github.com/aerdem4/lofo-importance)\n* [lrp_toolbox](https://github.com/sebastian-lapuschkin/lrp_toolbox)\n* [MindsDB](https://github.com/mindsdb/mindsdb)\n* [MLextend](http://rasbt.github.io/mlxtend/)\n* [ml-fairness-gym](https://github.com/google/ml-fairness-gym)\n* [ml_privacy_meter](https://github.com/privacytrustlab/ml_privacy_meter)\n* [OptBinning](https://github.com/guillermo-navas-palencia/optbinning)\n* [parity-fairness](https://pypi.org/project/parity-fairness/)\n* [PDPbox](https://github.com/SauceCat/PDPbox)\n* [pyBreakDown](https://github.com/MI2DataLab/pyBreakDown)\n* [PyCEbox](https://github.com/AustinRochford/PyCEbox)\n* [pyGAM](https://github.com/dswah/pyGAM)\n* [pymc3](https://github.com/pymc-devs/pymc3)\n* [pytorch-innvestigate](https://github.com/fgxaos/pytorch-innvestigate)\n* [rationale](https://github.com/taolei87/rcnn/tree/master/code/rationale)\n* [responsibly](https://github.com/ResponsiblyAI/responsibly)\n* [revise-tool](https://github.com/princetonvisualai/revise-tool)\n* [robustness](https://github.com/MadryLab/robustness)\n* [RISE](https://github.com/eclique/RISE)\n* [sage](https://github.com/iancovert/sage/)\n* [SALib](https://github.com/SALib/SALib)\n* [scikit-fairness](https://github.com/koaning/scikit-fairness)\n* [shap](https://github.com/slundberg/shap)\n* [shapley](https://github.com/benedekrozemberczki/shapley)\n* [Skater](https://github.com/datascienceinc/Skater)\n* [tensorfow/cleverhans](https://github.com/tensorflow/cleverhans)\n* [tensorflow/lucid](https://github.com/tensorflow/lucid)\n* [tensorflow/fairness-indicators](https://github.com/tensorflow/fairness-indicators)\n* [tensorflow/model-analysis](https://github.com/tensorflow/model-analysis)\n* [tensorflow/model-card-toolkit](https://github.com/tensorflow/model-card-toolkit)\n* [tensorflow/model-remediation](https://github.com/tensorflow/model-remediation)\n* [tensorflow/privacy](https://github.com/tensorflow/privacy)\n* [tensorflow/tcav](https://github.com/tensorflow/tcav)\n* [tensorfuzz](https://github.com/brain-research/tensorfuzz)\n* [TensorWatch](https://github.com/microsoft/tensorwatch)\n* [TextFooler](https://github.com/jind11/TextFooler)\n* [tf-explain](https://github.com/sicara/tf-explain)\n* [Themis](https://github.com/LASER-UMASS/Themis)\n* [themis-ml](https://github.com/cosmicBboy/themis-ml)\n* [treeinterpreter](https://github.com/andosa/treeinterpreter)\n* [woe](https://github.com/boredbird/woe)\n* [xai](https://github.com/EthicalML/xai)\n* [xdeep](https://github.com/datamllab/xdeep)\n* [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick)\n\n### R\n* [aif360](https://cran.r-project.org/web/packages/aif360/index.html)\n* [ALEPlot](https://cran.r-project.org/web/packages/ALEPlot/index.html)\n* [DrWhyAI](https://github.com/ModelOriented/DrWhy)\n* [DALEX](https://github.com/ModelOriented/DALEX)\n* [DALEXtra](https://cran.r-project.org/web/packages/DALEXtra/index.html)\n* [EloML](https://github.com/ModelOriented/EloML)\n* [ExplainPrediction](https://github.com/rmarko/ExplainPrediction)\n* [fastshap](https://github.com/bgreenwell/fastshap)\n* [fairness](https://cran.r-project.org/web/packages/fairness/index.html)\n* [fairmodels](https://github.com/ModelOriented/fairmodels)\n* [featureImportance](https://github.com/giuseppec/featureImportance)\n* [flashlight](https://github.com/mayer79/flashlight)\n* [forestmodel](https://cran.r-project.org/web/packages/forestmodel/index.html)\n* [fscaret](https://cran.r-project.org/web/packages/fscaret/)\n* [iBreakDown](https://github.com/ModelOriented/iBreakDown)\n* [ICEbox](https://cran.r-project.org/web/packages/ICEbox/index.html)\n* [iml](https://github.com/christophM/iml)\n* [ingredients](https://github.com/ModelOriented/ingredients)\n* [intepret](https://cran.r-project.org/web/packages/interpret/index.html)\n* [lightgbmExplainer](https://github.com/lantanacamara/lightgbmExplainer)\n* [lime](https://github.com/thomasp85/lime)\n* [live](https://cran.r-project.org/web/packages/live/index.html)\n* [mcr](https://github.com/aaronjfisher/mcr)\n* [modelDown](https://cran.r-project.org/web/packages/modelDown/index.html)\n* [modelOriented](https://github.com/ModelOriented)\n* [modelStudio](https://github.com/ModelOriented/modelStudio)\n* [pdp](https://bgreenwell.github.io/pdp/index.html)\n* [shapFlex](https://github.com/nredell/shapFlex)\n* [shapleyR](https://github.com/redichh/ShapleyR)\n* [shapper](https://cran.r-project.org/web/packages/shapper/index.html)\n* [smbinning](https://cran.r-project.org/web/packages/smbinning/index.html)\n* [vip](https://github.com/koalaverse/vip)\n* [xgboostExplainer](https://github.com/AppliedDataSciencePartners/xgboostExplainer)\n\n## Machine learning environment management tools\n\n* [dvc](https://dvc.org/)\n* [gigantum](https://github.com/gigantum)\n* [mlflow](https://mlflow.org/)\n* [mlmd](https://github.com/google/ml-metadata)\n* [modeldb](https://github.com/VertaAI/modeldb)\n* [whylabs](https://www.rsqrdai.org/)\n\n## Free Books\n\n* [An Introduction to Machine Learning Interpretability](https://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf)\n* [Explanatory Model Analysis](https://pbiecek.github.io/ema/)\n* [Fairness and Machine Learning](http://fairmlbook.org/)\n* [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)\n* [Responsible Machine Learning](https://www.h2o.ai/resources/ebook/responsible-machine-learning/) (requires email for now)\n\n## Government and Regulatory Documents\n\n* [12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)](https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1002/)\n* [A Regulatory Framework for AI: Recommendations for PIPEDA Reform](https://www.priv.gc.ca/en/about-the-opc/what-we-do/consultations/completed-consultations/consultation-ai/reg-fw_202011/)\n* [AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the Department of Defense](https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF)\n* [THE AIM INITIATIVE](https://www.dni.gov/files/ODNI/documents/AIM-Strategy.pdf)\n* [Aiming for truth, fairness, and equity in your company’s use of AI](https://www.ftc.gov/news-events/blogs/business-blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai)\n* [Algorithmic Accountability Act of 2019](https://www.wyden.senate.gov/imo/media/doc/Algorithmic%20Accountability%20Act%20of%202019%20Bill%20Text.pdf)\n* [ALGORITHM CHARTER FOR AOTEAROA NEW ZEALAND](https://data.govt.nz/assets/data-ethics/algorithm/Algorithm-Charter-2020_Final-English-1.pdf)\n* [Artificial Intelligence (AI) in the Securities Industry](https://www.finra.org/sites/default/files/2020-06/ai-report-061020.pdf)\n* [Article 22 EU GDPR](https://www.privacy-regulation.eu/en/article-22-automated-individual-decision-making-including-profiling-GDPR.htm)\n* [Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment - Shaping Europe’s digital future - European Commission](https://ec.europa.eu/digital-single-market/en/news/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment)\n* [Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology](https://media.defense.gov/2020/Jul/01/2002347967/-1/-1/1/DODIG-2020-098.PDF)\n* [A Primer on Artificial Intelligence in Securities Markets](https://www.cftc.gov/media/2846/LabCFTC_PrimerArtificialIntelligence102119/download)\n* [Biometric Information Privacy Act](https://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=3004&ChapterID=57)\n* [Booker Wyden Health Care Letters](https://www.scribd.com/document/437954989/Booker-Wyden-Health-Care-Letters#download)\n* [California Consumer Privacy Act (CCPA)](https://oag.ca.gov/privacy/ccpa)\n* [California Privacy Rights Act (CPRA)](https://www.oag.ca.gov/system/files/initiatives/pdfs/19-0021A1%20%28Consumer%20Privacy%20-%20Version%203%29_1.pdf)\n* [Consultation on the OPC’s Proposals for ensuring appropriate regulation of artificial intelligence](https://www.priv.gc.ca/en/about-the-opc/what-we-do/consultations/consultation-ai/pos_ai_202001/)\n* [Civil liability regime for artificial intelligence](https://www.europarl.europa.eu/doceo/document/TA-9-2020-0276_EN.pdf)\n* [Data Ethics Framework](https://strategy-staging.data.gov/assets/docs/data-ethics-framework-action-14-draft-2020-sep-2.pdf)\n* [DEVELOPING FINANCIAL SECTOR RESILIENCE IN A DIGITAL WORLD: SELECTED THEMES IN TECHNOLOGY AND RELATED RISKS](https://www.osfi-bsif.gc.ca/Eng/Docs/tchrsk.pdf)\n* [Directive on Automated Decision Making](https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592)\n* [Executive Order on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government](https://www.whitehouse.gov/presidential-actions/executive-order-promoting-use-trustworthy-artificial-intelligence-federal-government/)\n* [EEOC Letter (from U.S. senators re: hiring software)](https://www.bennet.senate.gov/public/_cache/files/0/a/0a439d4b-e373-4451-84ed-ba333ce6d1dd/672D2E4304D63A04CC3465C3C8BF1D21.letter-to-chair-dhillon.pdf)\n* [Facial Recognition and Biometric Technology Moratorium Act of 2020](https://drive.google.com/file/d/1gkTcjFtieMQdsQ01dmDa49B6HY9ZyKr8/view)\n* [Four Principles of Explainable Artificial Intelligence ](https://www.nist.gov/system/files/documents/2020/08/17/NIST%20Explainable%20AI%20Draft%20NISTIR8312%20%281%29.pdf)\n* [General principles for the use of Artificial Intelligence in the financial sector](https://www.dnb.nl/media/jkbip2jc/general-principles-for-the-use-of-artificial-intelligence-in-the-financial-sector.pdf)\n* [Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (French)](https://acpr.banque-france.fr/sites/default/files/medias/documents/20200612_gouvernance_evaluation_ia.pdf)\n* [Innovation spotlight: Providing adverse action notices when using AI/ML models](https://www.consumerfinance.gov/about-us/blog/innovation-spotlight-providing-adverse-action-notices-when-using-ai-ml-models/)\n* [Office of Management and Budget Guidance for Regulation of Artificial Intelligence Applications](https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf) (Finalized Nov. 2020)\n* [On Artificial Intelligence - A European approach to excellence and trust](https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf)\n* [Opinion of the German Data Ethics Commission](https://www.bmjv.de/SharedDocs/Downloads/DE/Themen/Fokusthemen/Gutachten_DEK_EN.pdf?__blob=publicationFile&v=2)\n* [Principles of Artificial Intelligence Ethics for the Intelligence Community](https://www.intel.gov/principles-of-artificial-intelligence-ethics-for-the-intelligence-community)\n* [Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)](https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence)\n* [Psychological Foundations of Explainability and Interpretability in Artificial Intelligence](https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8367.pdf)\n* [Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines)\n* [Questions from the Commission on Protecting Privacy and Preventing Discrimination](https://auditor.utah.gov/wp-content/uploads/sites/6/2021/02/Office-of-the-State-Auditor-Questions-to-help-Procuring-Agencies-_-Entities-with-Software-Procurement-Feb-1-2021-Final.pdf)\n* [RE: Use of External Consumer Data and Information Sources in Underwriting for Life Insurance](https://www.dfs.ny.gov/industry_guidance/circular_letters/cl2019_01)\n* [Singapore Personal Data Protection Commission (PDPC) Model Artificial Intelligence Governance Framework](https://www.pdpc.gov.sg/Help-and-Resources/2020/01/Model-AI-Governance-Framework)\n* [SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT](https://www.federalreserve.gov/supervisionreg/srletters/sr1107a1.pdf)\n* [U.K. Information Commissioner's Office (ICO) AI Audting Framework (overview series)](https://ico.org.uk/about-the-ico/news-and-events/ai-blog-an-overview-of-the-auditing-framework-for-artificial-intelligence-and-its-core-components/)\n* [Artificial Intelligence/Machine Learning (AI/ML)-Based: Software as a Medical Device (SaMD) Action Plan](https://www.fda.gov/media/145022/download) (Updated Jan. 2021)\n* [U.S. House of Representatives Resolution on AI Strategy](https://hurd.house.gov/sites/hurd.house.gov/files/HURDTX_AI%20Res.pdf)\n* [Using Artificial Intelligence and Algorithms](https://www.ftc.gov/news-events/blogs/business-blog/2020/04/using-artificial-intelligence-algorithms)\n\n\n## Other Interpretability and Fairness Resources and Lists\n\n* [8 Principles of Responsible ML](https://ethical.institute/principles.html)\n* [ACM FAT* 2019 Youtube Playlist](https://www.youtube.com/playlist?list=PLXA0IWa3BpHk7fE8IH6wXNEfAZyr3A5Yb)\n* [Adversarial ML Threat Matrix](https://github.com/mitre/advmlthreatmatrix)\n* [AI Tools and Platforms](https://docs.google.com/spreadsheets/u/2/d/10pPQYmyNnYb6zshOKxBjJ704E0XUj2vJ9HCDfoZxAoA/htmlview#)\n* [AI Ethics Guidelines Global Inventory](https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/)\n* [AI Incident Database](http://aiid.partnershiponai.org/)\n* [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](http://sameersingh.org/files/papers/allennlp-interpret-demo-emnlp19.pdf)\n* [Algorithms and prejudice](https://www.thesaturdaypaper.com.au/news/politics/2019/12/07/algorithms-and-prejudice/15756372009195)\n* [Awesome interpretable machine learning](https://github.com/lopusz/awesome-interpretable-machine-learning) ;)\n* [Awesome machine learning operations](https://github.com/EthicalML/awesome-machine-learning-operations)\n* [Awful AI](https://github.com/daviddao/awful-ai)\n* [algoaware](https://www.algoaware.eu/)\n* [BIML Interactive Machine Learning Risk Framework](https://berryvilleiml.com/interactive/)\n* [Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models](https://go.immuta.com/beyond-explainability-white-paper)\n* [criticalML](https://github.com/rockita/criticalML)\n* [Data Feminism](https://mitpress.mit.edu/books/data-feminism)\n* [Dealing with Bias and Fairness in AI/ML/Data Science Systems](https://docs.google.com/presentation/d/17o_NzplYua5fcJFuGcy1V1-5GFAHk7oHAF4dN44NkUE)\n* [Debugging Machine Learning Models (ICLR workshop proceedings)](https://debug-ml-iclr2019.github.io/)\n* [Decision Points in AI Governance](https://cltc.berkeley.edu/wp-content/uploads/2020/05/Decision_Points_AI_Governance.pdf)\n* [De-identification Tools](https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/focus-areas/de-id/tools)\n* [Deep Insights into Explainability and Interpretability of Machine Learning Algorithms and Applications to Risk Management](https://ww2.amstat.org/meetings/jsm/2019/onlineprogram/AbstractDetails.cfm?abstractid=303053)\n* [Distill](https://distill.pub)\n* [Faces in the Wild Benchmark Data](https://github.com/visionjo/facerec-bias-bfw)\n* [Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship](https://www.fatml.org/resources/relevant-scholarship)\n* [From Principles to Practice: An interdisciplinary framework to operationalise AI ethics](https://www.ai-ethics-impact.org/resource/blob/1961130/c6db9894ee73aefa489d6249f5ee2b9f/aieig---report---download-hb-data.pdf)\n* [How will the GDPR impact machine learning?](https://www.oreilly.com/radar/how-will-the-gdpr-impact-machine-learning/)\n* [Machine Learning Ethics References](https://github.com/radames/Machine-Learning-Ethics-References)\n* [Machine Learning Interpretability Resources](https://github.com/h2oai/mli-resources)\n* [Machine Learning: Considerations for fairly and transparently expanding access to credit](http://info.h2o.ai/rs/644-PKX-778/images/Machine%20Learning%20-%20Considerations%20for%20Fairly%20and%20Transparently%20Expanding%20Access%20to%20Credit.pdf)\n* [MIT AI Ethics Reading Group](https://mitaiethics.github.io/)\n* [On the Responsibility of Technologists: A Prologue and Primer](https://algo-stats.info/2018/04/15/on-the-responsibility-of-technologists-a-prologue-and-primer/)\n* [private-ai-resources](https://github.com/OpenMined/private-ai-resources)\n* [Problems with Shapley-value-based explanations as feature importance measures](https://arxiv.org/pdf/2002.11097v1.pdf)\n* [Real-World Model Debugging Strategies](https://medium.com/@jphall_22520/strategies-for-model-debugging-aa822f1097ce)\n* [ResponsibleAI](https://romanlutz.github.io/ResponsibleAI/)\n* [Robust ML](https://www.robust-ml.org/)\n* [Safe and Reliable Machine Learning](https://www.dropbox.com/s/sdu26h96bc0f4l7/FAT19-AI-Reliability-Final.pdf?dl=0)\n* [Sample AI Incident Response Checklist](https://bnh-ai.github.io/resources/)\n* [Ten Questions on AI Risk](https://fpf.org/wp-content/uploads/2020/06/Ten-Questions-on-AI-Risk-FPF.pdf)\n* [Testing and Debugging in Machine Learning](https://developers.google.com/machine-learning/testing-debugging)\n* [Troubleshooting Deep Neural Networks](http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf)\n* [Warning Signs: The Future of Privacy and Security in an Age of Machine Learning](https://fpf.org/wp-content/uploads/2019/09/FPF_WarningSigns_Report.pdf)\n* [When Not to Trust Your Explanations](https://docs.google.com/presentation/d/10a0PNKwoV3a1XChzvY-T1mWudtzUIZi3sCMzVwGSYfM/edit)\n* [XAI Resources](https://github.com/pbiecek/xai_resources)\n* [You Created A Machine Learning Application Now Make Sure It's Secure](https://www.oreilly.com/ideas/you-created-a-machine-learning-application-now-make-sure-its-secure)\n\n## Review and General Papers\n\n* [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://arxiv.org/pdf/1811.10104.pdf)\n* [A Comparative Study of Fairness-Enhancing Interventions in Machine Learning](https://arxiv.org/pdf/1802.04422.pdf)\n* [A Survey Of Methods For Explaining Black Box Models](https://arxiv.org/pdf/1802.01933.pdf)\n* [A Marauder’s Map of Security and Privacy in Machine Learning](https://arxiv.org/pdf/1811.01134.pdf)\n* [Challenges for Transparency](https://arxiv.org/pdf/1708.01870.pdf)\n* [Closing the AI Accountability Gap](https://arxiv.org/pdf/2001.00973.pdf)\n* [Explaining by Removing: A Unified Framework for Model Explanation](https://arxiv.org/abs/2011.14878)\n* [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://arxiv.org/pdf/1806.00069.pdf)\n* [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://arxiv.org/abs/1902.01876v1)\n* [Interpretable Machine Learning: Definitions, Methods, and Applications](https://arxiv.org/abs/1901.04592)\n* [Limitations of Interpretable Machine Learning](https://compstat-lmu.github.io/iml_methods_limitations/)\n* [Machine Learning Explainability in Finance](https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2019/machine-learning-explainability-in-finance-an-application-to-default-risk-analysis)\n* [On the Art and Science of Machine Learning Explanations](https://arxiv.org/pdf/1810.02909.pdf)\n* [Please Stop Explaining Black Box Models for High-Stakes Decisions](https://arxiv.org/pdf/1811.10154.pdf)\n* [Software Engineering for Machine Learning: A Case Study](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/amershi-icse-2019_Software_Engineering_for_Machine_Learning.pdf)\n* [The Mythos of Model Interpretability](https://arxiv.org/pdf/1606.03490.pdf)\n* [Towards A Rigorous Science of Interpretable Machine Learning](https://arxiv.org/pdf/1702.08608.pdf)\n* [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://arxiv.org/pdf/2004.07213.pdf)\n* [The Security of Machine Learning](https://people.eecs.berkeley.edu/~adj/publications/paper-files/SecML-MLJ2010.pdf)\n* [Techniques for Interpretable Machine Learning](https://arxiv.org/pdf/1808.00033.pdf)\n* [Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda](https://dl.acm.org/citation.cfm?id=3174156)\n* [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://arxiv.org/pdf/2011.03395.pdf)\n\n## Classes\n\n* [An Introduction to Data Ethics](https://www.scu.edu/ethics/focus-areas/technology-ethics/resources/an-introduction-to-data-ethics/)\n* [Certified Ethical Emerging Technologist](https://certnexus.com/certification/ceet/)\n* [Fairness in Machine Learning](https://fairmlclass.github.io/)\n* [Fast.ai Data Ethics course](http://ethics.fast.ai/syllabus/#lesson-2-bias--fairness)\n* [Human-Center Machine Learning](http://courses.mpi-sws.org/hcml-ws18/)\n* [Introduction to Responsible Machine Learning](https://jphall663.github.io/GWU_rml/)\n* [Trustworthy Deep Learning](https://berkeley-deep-learning.github.io/cs294-131-s19/)\n\n\n## Interpretable (\"Whitebox\") or Fair Modeling Packages\n\n### C/C++\n\n* [Born-again Tree Ensembles](https://github.com/vidalt/BA-Trees)\n* [Certifiably Optimal RulE ListS](https://github.com/nlarusstone/corels)\n\n### Python\n\n* [Bayesian Case Model](https://users.cs.duke.edu/~cynthia/code/BCM.zip)\n* [Bayesian Ors-Of-Ands](https://github.com/wangtongada/BOA)\n* [Bayesian Rule List (BRL)](https://users.cs.duke.edu/~cynthia/code/BRL_supplement_code.zip)\n* [Explainable Boosting Machine (EBM)/GA2M](https://github.com/interpretml/interpret)\n* [fair-classification](https://github.com/mbilalzafar/fair-classification)\n* [Falling Rule List (FRL)](https://users.cs.duke.edu/~cynthia/code/falling_rule_list.zip)\n* H2O-3\n  * [Penalized Generalized Linear Models](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlinearestimator)\n  * [Monotonic GBM](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogradientboostingestimator)\n  * [Sparse Principal Components (GLRM)](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlowrankestimator)\n* [learning-fair-representations](https://github.com/zjelveh/learning-fair-representations)\n* [Optimal Sparse Decision Trees](https://github.com/xiyanghu/OSDT)\n* [Monotonic](http://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html) [XGBoost](http://xgboost.readthedocs.io/en/latest/)\n* [Multilayer Logical Perceptron (MLLP)](https://github.com/12wang3/mllp)\n* [pyGAM](https://github.com/dswah/pyGAM)\n* [pySS3](https://github.com/sergioburdisso/pyss3)\n* [Risk-SLIM](https://github.com/ustunb/risk-SLIM)\n* Scikit-learn\n  * [Decision Trees](http://scikit-learn.org/stable/modules/tree.html)\n  * [Generalized Linear Models](http://scikit-learn.org/stable/modules/linear_model.html)\n  * [Sparse Principal Components](http://scikit-learn.org/stable/modules/decomposition.html#sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca)\n* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys)\n* [skope-rules](https://github.com/scikit-learn-contrib/skope-rules)\n* [Super-sparse Linear Integer models (SLIMs)](https://github.com/ustunb/slim-python)\n* [tensorflow/lattice](https://github.com/tensorflow/lattice)\n* [This Looks Like That](https://github.com/cfchen-duke/ProtoPNet)\n\n### R\n\n* [arules](https://cran.r-project.org/web/packages/arules/index.html)\n* [Causal SVM](https://github.com/shangtai/githubcausalsvm)\n* [elasticnet](https://cran.r-project.org/web/packages/elasticnet/index.html)\n* [Explainable Boosting Machine (EBM)/GA2M](https://cran.r-project.org/web/packages/interpret/index.html)\n* [gam](https://cran.r-project.org/web/packages/gam/index.html)\n* [glm2](https://cran.r-project.org/web/packages/glm2/)\n* [glmnet](https://cran.r-project.org/web/packages/glmnet/index.html)\n* H2O-3\n  * [Penalized Generalized Linear Models](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.glm.html)\n  * [Monotonic GBM](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.gbm.html)\n  * [Sparse Principal Components (GLRM)](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.glrm.html)\n* [Monotonic](http://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html) [XGBoost](http://xgboost.readthedocs.io/en/latest/)\n* [quantreg](https://cran.r-project.org/web/packages/quantreg/index.html)\n* [rpart](https://cran.r-project.org/web/packages/rpart/index.html)\n* [RuleFit](http://statweb.stanford.edu/~jhf/R_RuleFit.html)\n* [Scalable Bayesian Rule Lists (SBRL)](https://users.cs.duke.edu/~cynthia/code/sbrl_1.0.tar.gz)\n\n## AI Incident Tracker\n\n* [Mar 1988 - A blot on the profession](https://www.bmj.com/content/296/6623/657)\n* [Jan 2010 - Are Face-Detection Cameras Racist?](http://content.time.com/time/business/article/0,8599,1954643,00.html)\n* [Jul 2015 - Google says sorry for racist auto-tag in photo app](https://www.theguardian.com/technology/2015/jul/01/google-sorry-racist-auto-tag-photo-app)\n* [Mar 2016 - Here Are the Microsoft Twitter Bot’s Craziest Racist Rants](https://gizmodo.com/here-are-the-microsoft-twitter-bot-s-craziest-racist-ra-1766820160)\n* [Jun 2016 - Google faulted for racial bias in image search results for black teenagers](https://www.washingtonpost.com/news/morning-mix/wp/2016/06/10/google-faulted-for-racial-bias-in-image-search-results-for-black-teenagers/)\n* [Oct 2016 - 'Rogue' Algorithm Blamed for Historic Crash of the British Pound](https://gizmodo.com/rogue-algorithm-blamed-for-historic-crash-of-the-britis-1787523587)\n* [Oct 2016 - Crime-prediction tool PredPol amplifies racially biased policing, study shows](https://www.mic.com/articles/156286/crime-prediction-tool-pred-pol-only-amplifies-racially-biased-policing-study-shows)\n* [May 2017 - Houston Schools Must Face Teacher Evaluation Lawsuit](https://www.courthousenews.com/houston-schools-must-face-teacher-evaluation-lawsuit/)\n* [Jun 2017 - When a Computer Program Keeps You in Jail](https://www.nytimes.com/2017/06/13/opinion/how-computers-are-harming-criminal-justice.html)\n* [Jun 2017 - Antitrust: Commission fines Google €2.42 billion for abusing dominance as search engine by giving illegal advantage to own comparison shopping service](https://ec.europa.eu/commission/presscorner/detail/en/IP_17_1784)\n* [Jul 2017 - ‘Balls have zero to me to me’: What happened when Facebook’s AI chatbots Bob & Alice created their own language](https://analyticsindiamag.com/facebook-ai-chatbots-created-their-own-language/)\n* [Jul 2017 - YouTube: Boston Dynamics' Atlas Falls Over After Demo at the Congress of Future Scientists and Technologists](https://www.youtube.com/watch?v=TxobtWAFh8o)\n* [Jul 2017 - Royal Free - Google DeepMind trial failed to comply with data protection law](https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/)\n* [Nov 2017 - Hackers Say They've Broken Face ID a Week After iPhone X Release](https://www.wired.com/story/hackers-say-broke-face-id-security/)\n* [Nov 2017 - India’s Friendly Robot Mitra Not Only Greets VIPs On The Stage, But Also Parties Like A Rockstar](https://analyticsindiamag.com/mitra-robot-ivanka-trump-modi-ges/) (Mitra trips over Ivanka Trump/PM Modi introduction)\n* [Jan 2018 - YouTube: CES 2018: Robot refuses to co-operate with LG chief - BBC News](https://www.youtube.com/watch?v=tQMtbWwbduA)\n* [Feb 2018 - Study finds gender and skin-type bias in commercial artificial-intelligence systems](http://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212)\n* [Mar 2018 - Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam](https://www.nytimes.com/2018/03/19/technology/uber-driverless-fatality.html)\n* [Mar 2018 - AI-Assisted Fake Porn Is Here and We're All F***ed](https://www.vice.com/en_us/article/bj5and/ai-assisted-fake-porn-is-here-and-were-all-fucked)\n* [Jun 2018 - Facebook sent a doctor on a secret mission to ask hospitals to share patient data](https://www.cnbc.com/2018/04/05/facebook-building-8-explored-data-sharing-agreement-with-hospitals.html)\n* [Jul 2018 - Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots](https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28)\n* [Jul 2018 - IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show](https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/)\n* [Oct 2018 - Amazon scraps 'sexist AI' recruiting tool that showed bias against women](https://www.telegraph.co.uk/technology/2018/10/10/amazon-scraps-sexist-ai-recruiting-tool-showed-bias-against/)\n* [Nov 2018 - Facial recognition system in China mistakes bus ad for jaywalker](https://www.biometricupdate.com/201811/facial-recognition-system-in-china-mistakes-bus-ad-for-jaywalker)\n* [Dec 2018 - AI start-up that scanned babysitters halts launch following Post Report](https://www.washingtonpost.com/technology/2018/12/14/ai-start-up-that-scanned-babysitters-halts-launch-following-post-report/)\n* [Jan 2019 - Cambridge Analytica’s parent pleads guilty to breaking UK data law](https://techcrunch.com/2019/01/09/cambridge-analyticas-parent-pleads-guilty-to-breaking-uk-data-law/)\n* [Apr 2019 - Facebook Executive Testifies on AI Failure to Detect the Christchurch Mosque Shooting Video](https://fortune.com/2019/04/24/facebook-new-zealand-terrorism-artificial-intelligence-ai/)\n* [May 2019 - Investor Sues After an AI’s Automated Trades Cost Him $20 Million](https://futurism.com/investing-lawsuit-ai-trades-cost-millions)\n* [May 2019 - \nMillions of people uploaded photos to the Ever app. Then the company used them to develop facial recognition tools.](https://www.nbcnews.com/tech/security/millions-people-uploaded-photos-ever-app-then-company-used-them-n1003371)\n* [Jun 2019 - Google and the University of Chicago Are Sued Over Data Sharing](https://www.nytimes.com/2019/06/26/technology/google-university-chicago-data-sharing-lawsuit.html)\n* [Aug 2019 - LGBTQ+ creators file lawsuit against YouTube for discrimination](https://thenextweb.com/google/2019/08/14/lgbtq-youtube-discrimination-lawsuit/)\n* [Sep 2019 - The viral selfie app ImageNet Roulette seemed fun – until it called me a racist slur](https://www.theguardian.com/technology/2019/sep/17/imagenet-roulette-asian-racist-slur-selfie)\n* [Sep 2019 - Scammer Successfully Deepfaked CEO's Voice To Fool Underling Into Transferring $243,000](https://gizmodo.com/scammer-successfully-deepfaked-ceos-voice-to-fool-under-1837835066)\n* [Oct 2019 - Oh dear... AI models used to flag hate speech online are, er, racist against black people](https://www.theregister.com/2019/10/11/ai_black_people/)\n* [Oct 2019 - Dissecting racial bias in an algorithm used to manage the health of populations](https://science.sciencemag.org/content/366/6464/447)\n* [Nov 2019 - \nNY regulator investigating Apple Card for possible gender bias](https://www.nbcnews.com/tech/apple/ny-regulator-investigating-apple-card-possible-gender-bias-n1079581)\n* [Nov 2019 - Chinese-style facial recognition technology is trialled in Australian schools to register pupils - sparking major privacy concerns](https://www.dailymail.co.uk/news/article-7642411/Australian-schools-trial-facial-recognition-technology-attendance.html)\n* [Dec 2019 - Tenants sounded the alarm on facial recognition in their buildings. Lawmakers are listening](https://www.msn.com/en-us/news/politics/tenants-sounded-the-alarm-on-facial-recognition-in-their-buildings-lawmakers-are-listening/ar-BBYnaqB)\n* [Dec 2019 - Researchers bypass airport and payment facial recognition systems using masks](https://www.engadget.com/2019-12-16-facial-recognition-fooled-masks.html)\n* [Jan 2020 - Atlantic Plaza Towers tenants won a halt to facial recognition in their building: Now they’re calling on a moratorium on all residential use](https://medium.com/@AINowInstitute/atlantic-plaza-towers-tenants-won-a-halt-to-facial-recognition-in-their-building-now-theyre-274289a6d8eb)\n* [Jan 2020 - Trivago misled consumers about hotel room rates](https://www.accc.gov.au/media-release/trivago-misled-consumers-about-hotel-room-rates)\n* [Feb 2020 - An Indian politician is using deepfake technology to win new voters](https://www.technologyreview.com/2020/02/19/868173/an-indian-politician-is-using-deepfakes-to-try-and-win-voters/)\n* [Feb 2020 - Suckers List: How Allstate’s Secret Auto Insurance Algorithm Squeezes Big Spenders](https://themarkup.org/allstates-algorithm/2020/02/25/car-insurance-suckers-list)\n* [Feb 2020 - Tesla Autopilot gets tricked into accelerating from 35 to 85 mph with modified speed limit sign](https://electrek.co/2020/02/19/tesla-autopilot-tricked-accelerate-speed-limit-sign/)\n* [Mar 2020 - Netherlands: Court Prohibits Government’s Use of AI Software to Detect Welfare Fraud](https://www.loc.gov/law/foreign-news/article/netherlands-court-prohibits-governments-use-of-ai-software-to-detect-welfare-fraud/)\n* [Mar 2020 - The End of Starsky Robotics](https://medium.com/starsky-robotics-blog/the-end-of-starsky-robotics-acb8a6a8a5f5)\n* [Apr 2020 - Google apologizes after its Vision AI produced racist results](https://algorithmwatch.org/en/story/google-vision-racism/)\n* [Apr 2020 - Google’s medical AI was super accurate in a lab. Real life was a different story.](https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/)\n* [May 2020 - Researchers find major demographic differences in speech recognition accuracy](https://www.biometricupdate.com/202003/researchers-find-major-demographic-differences-in-speech-recognition-accuracy)\n* [May 2020 - Access Denied: Faulty Automated Background Checks Freeze Out Renters](https://themarkup.org/locked-out/2020/05/28/access-denied-faulty-automated-background-checks-freeze-out-renters)\n* [May 2020 - A.C.L.U. Accuses Clearview AI of Privacy ‘Nightmare Scenario’](https://www.nytimes.com/2020/05/28/technology/clearview-ai-privacy-lawsuit.html)\n* [May 2020 - Walmart Employees Are Out to Show Its Anti-Theft AI Doesn't Work](https://www.wired.com/story/walmart-shoplifting-artificial-intelligence-everseen/)\n* [May 2020 - Robodebt removed humans from Human Services, and the Government is facing the consequences](https://www.abc.net.au/news/2020-05-30/robodebt-stuart-robert-scott-morrison/12303322)\n* [May 2020 - The Most Devastating Software Mistake Of All Time. Why Is the Imperial Model Under Criticism?](https://analyticsindiamag.com/the-most-devastating-software-mistake-of-all-time-why-is-the-imperial-model-under-criticism/)\n* [Jun 2020 - Government’s Use of Algorithm Serves Up False Fraud Charges](https://undark.org/2020/06/01/michigan-unemployment-fraud-algorithm/)\n* [Jun 2020 - Microsoft's robot editor confuses mixed-race Little Mix singers](https://www.theguardian.com/technology/2020/jun/09/microsofts-robot-journalist-confused-by-mixed-race-little-mix-singers)\n* [Jun 2020 - Tweet: \"This algorithm probably made this mistake ...\"](https://twitter.com/kareem_carr/status/1274462329653137419) (President Obama de-blurred into white male)\n* [Jun 2020 - Detroit Police Chief: Facial Recognition Software Misidentifies 96% of the Time](https://www.vice.com/en_us/article/dyzykz/detroit-police-chief-facial-recognition-software-misidentifies-96-of-the-time)\n* [Jun 2020 - Wrongfully Accused by an Algorithm](https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html)\n* [Jun 2020 - An Algorithm that \"Predicts\" Criminality Based on a Face Sparks a Furor](https://www.wired.com/story/algorithm-predicts-criminality-based-face-sparks-furor/)\n* [Jun 2020 - PwC facial recognition tool criticised for home working privacy invasion](https://www.personneltoday.com/hr/pwc-facial-recognition-tool-criticised-for-home-working-privacy-invasion/)\n* [Jun 2020 - Santa Cruz becomes the first U.S. city to ban predictive policing](https://www.latimes.com/california/story/2020-06-26/santa-cruz-becomes-first-u-s-city-to-ban-predictive-policing)\n* [Jun 2020 - YouTube Sued for Race Discrimination, Profiting from Hate Speech](https://news.bloomberglaw.com/class-action/youtube-sued-for-race-discrimination-profiting-from-hate-speech)\n* [Jul 2020 - ISIS 'still evading detection on Facebook', report says](https://www.bbc.com/news/technology-53389657)\n* [Jul 2020 - Meet the Secret Algorithm That's Keeping Students Out of College](https://www.wired.com/story/algorithm-set-students-grades-altered-futures/)\n* [Jul 2020 - Rite Aid deployed facial recognition systems in hundreds of U.S. stores](https://www.reuters.com/investigates/special-report/usa-riteaid-software/)\n* [Jul 2020 - Tweet: \"Oh, dear ...\" (GPT-3 anti-semitism)](https://mobile.twitter.com/jsellenberg/status/1289018551806894081)\n* [Jul 2020 - Google Ad Portal Equated “Black Girls” with Porn](https://themarkup.org/google-the-giant/2020/07/23/google-advertising-keywords-black-girls)\n* [Jul 2020 - Facial biometrics training dataset leads to BIPA lawsuits against Amazon, Alphabet and Microsoft](https://www.biometricupdate.com/202007/facial-biometrics-training-dataset-leads-to-bipa-lawsuits-against-amazon-alphabet-and-microsoft)\n* [Jul 2020 - POLICE SURVEILLED GEORGE FLOYD PROTESTS WITH HELP FROM TWITTER-AFFILIATED STARTUP DATAMINR](https://theintercept.com/2020/07/09/twitter-dataminr-police-spy-surveillance-black-lives-matter-protests/)\n* [Jul 2020 - AI-Powered ‘Genderify’ Platform Shut Down After Bias-Based Backlash](https://syncedreview.com/2020/07/30/ai-powered-genderify-platform-shut-down-after-bias-based-backlash/)\n* [Aug 2020 - Police use of facial recognition unlawfully breached privacy rights, says Court of Appeal ruling](https://news.sky.com/story/police-use-of-facial-recognition-unlawfully-breached-privacy-rights-says-court-of-appeal-ruling-12047012)\n* [Aug 2020 - There is nothing 'fair' about judging A-levels by algorithm](https://www.telegraph.co.uk/opinion/2020/08/12/nothing-fair-judging-a-levels-algorithm/)\n* [Aug 2020 - When algorithms define kids by postcode: UK exam results chaos reveal too much reliance on data analytics](https://www.zdnet.com/article/when-algorithms-define-kids-by-postcode-uk-exam-results-chaos-reveal-too-much-reliance-on-data-analytics/)\n* [Aug 2020 - Macy’s hit with privacy lawsuit over alleged use of controversial facial recognition software](https://www.chicagotribune.com/business/ct-biz-macys-lawsuit-clearview-facial-recognition-20200811-mstcyf7wufdjvbanpv6ehjtvni-story.html)\n* [Aug 2020 - Google’s Advertising Platform Is Blocking Articles About Racism](https://slate.com/technology/2020/08/googles-ad-exchange-blocking-articles-about-racism.html) \n* [Aug 2020 - Home Office drops 'racist' algorithm from visa decisions](https://www.bbc.com/news/technology-53650758)\n* [Aug 2020 - De Blasio Will Reassess NYPD's Use Of Facial Recognition Tech After Protester Arrest](https://gothamist.com/news/de-blasio-will-reassess-nypds-use-facial-recognition-tech-after-protester-arrest)\n* [Aug 2020 - Facebook algorithm recommending Holocaust denial and fascist content, report finds](https://www.independent.co.uk/news/uk/home-news/facebook-holocaust-denial-fascist-right-wing-algorithm-report-a9673171.html)\n* [Aug 2020 - Report: AI Company Leaks Over 2.5M Medical Records](https://www.pcmag.com/news/report-ai-company-leaks-over-25m-medical-records)\n* [Aug 2020 - Watchdog investigates Barclays for spying on staff](https://www.advisen.com/tools/fpnproc/fpns/articles_new_5/P/374532561.html)\n* [Aug 2020 - PopID’s face-based payments pose privacy and security risks](https://venturebeat.com/2020/08/27/popids-face-based-payments-pose-privacy-and-security-risks/)\n* [Aug 2020 - Tinder charges older people more](https://www.choice.com.au/electronics-and-technology/internet/using-online-services/articles/tinder-plus-costs-more-if-youre-older)\n* [Aug 2020 - Uber and Lyft pricing algorithms charge more in non-white areas](https://www.newscientist.com/article/2246202-uber-and-lyft-pricing-algorithms-charge-more-in-non-white-areas/)\n* [Sep 2020 - Pasco’s sheriff uses data to guess who will commit crime. Then deputies ‘hunt down’ and harass them](https://www.tampabay.com/news/pasco/2020/09/03/pascos-sheriff-uses-data-to-guess-who-will-commit-crime-then-deputies-hunt-down-and-harass-them/)\n* [Sep 2020 - The Met Police didn’t check if facial recognition tech was racist before trialling it](https://tech.newstatesman.com/public-sector/the-met-police-didnt-check-if-facial-recognition-tech-was-racist-before-trialling-it)\n* [Sep 2020 - These students figured out their tests were graded by AI — and the easy way to cheat](https://www.theverge.com/2020/9/2/21419012/edgenuity-online-class-ai-grading-keyword-mashing-students-school-cheating-algorithm-glitch)\n* [Sep 2020 - Google says Street View maps algorithm error blurred out Hong Kong protest graffiti aimed at Xi Jinping](https://hongkongfp.com/2020/09/07/google-says-street-view-maps-algorithm-error-blurred-out-hong-kong-protest-graffiti-aimed-at-xi-jinping/)\n* [Sep 2020 - AI attempts to ease fear of robots, blurts out it can’t ‘avoid destroying humankind’](https://www.skynews.com.au/details/_6189352902001)\n* [Sep 2020 - Ola is facing a drivers’ legal challenge over data access rights and algorithmic management](https://techcrunch.com/2020/09/10/ola-is-facing-a-drivers-legal-challenge-over-data-access-rights-and-algorithmic-management/)\n* [Sep 2020 - Instagram apologizes for removing images of Black British model](https://www.thejakartapost.com/life/2020/09/12/instagram-apologizes-for-removing-images-of-black-british-model.html)\n* [Sep 2020 - Tesla owner in Canada charged with ‘sleeping’ while driving over 90 mph](https://www.theverge.com/2020/9/18/21445168/tesla-driver-sleeping-police-charged-canada-autopilot)\n* [Sep 2020 - Female historians and male nurses do not exist, Google Translate tells its European users](https://algorithmwatch.org/en/story/google-translate-gender-bias/)\n* [Sep 2020 - Twitter is looking into why its photo preview appears to favor white faces over Black faces](https://www.theverge.com/2020/9/20/21447998/twitter-photo-preview-white-black-faces)\n* [Sep 2020 - Facebook Live’s New Music Terms of Service Unfairly Impact Artists](https://news.bloomberglaw.com/ip-law/facebook-lives-new-music-terms-of-service-unfairly-impact-artists)\n* [Sep 2020 - CoreLogic’s screening algorithm may have discriminated against renters: lawsuit](https://therealdeal.com/2020/09/25/corelogics-screening-algorithm-may-have-discriminated-against-renters-lawsuit/)\n* [Sep 2020 - Gradient Photo Editing App Criticized Over 'Racist' AI Face Feature](https://screenrant.com/gradient-photo-editing-app-racist-ai-face-feature/)\n* [Sep 2020 - ExamSoft’s remote bar exam sparks privacy and facial recognition concerns](https://venturebeat.com/2020/09/29/examsofts-remote-bar-exam-sparks-privacy-and-facial-recognition-concerns/)\n* [Sep 2020 - \"Trustworthiness\" Study Is Basically Phrenology, Annoying Scientists, Historians, Just About Everyone](https://www.iflscience.com/technology/trustworthiness-study-is-basically-phrenology-annoying-scientists-historians-just-about-everyone/)\n* [Sep 2020 - IBM faces another age-discrimination lawsuit in Austin](https://www.bizjournals.com/austin/news/2020/09/29/ibm-hit-with-another-age-discrimination-lawsuit.html)\n* [Sep 2020 - Your favorite A.I. language tool is toxic](https://fortune.com/2020/09/29/artificial-intelligence-openai-gpt3-toxic/)\n* [Sep 2020 - Catching Amazon in a lie](https://www.revealnews.org/episodes/catching-amazon-in-a-lie/)\n* [Sep 2020 - Tweet: \"A faculty member has been asking how to stop Zoom from removing his head ...\"](https://twitter.com/colinmadland/status/1307111818981146626) (Zoom erasing darker-skinned professor's head)\n* [Sep 2020 - Whistleblowers charge CEO of NJ firm with inflating AI capability, calling employees “dirty Indians”](https://medcitynews.com/2020/09/whistleblowers-charge-ceo-of-nj-firm-with-inflating-ai-capability-calling-employees-dirty-indians/?rf=1)\n* [Oct 2020 - Jewish Baby Stroller Image Algorithm](https://www.timebulletin.com/jewish-baby-stroller-image-algorithm/)\n* [Oct 2020 - Instagram blames GDPR for failure to tackle rampant self-harm and eating-disorder images](https://www.telegraph.co.uk/technology/2020/10/04/exclusive-instagram-blames-gdpr-failure-tackle-rampant-self/)\n* [Oct 2020 - UK passport photo checker shows bias against dark-skinned women](https://www.bbc.co.uk/news/amp/technology-54349538)\n* [Oct 2020 - States Say the Online Bar Exam Was a Success. The Test-Taker Who Peed in His Seat Disagrees](https://www.law.com/2020/10/07/states-say-the-online-bar-exam-was-a-success-the-test-taker-who-peed-in-his-seat-disagrees/)\n* [Oct 2020 - Tiny Changes Let False Claims About COVID-19, Voting Evade Facebook Fact Checks](https://www.npr.org/2020/10/09/921791419/tiny-changes-let-false-claims-about-covid-19-voting-evade-facebook-fact-checks)\n* [Oct 2020 - Leaving Cert: Why the Government deserves an F for algorithms](https://www.irishtimes.com/business/technology/leaving-cert-why-the-government-deserves-an-f-for-algorithms-1.4374801)\n* [Oct 2020 - Lawsuit alleges biometric privacy violations from face recognition algorithm training](https://www.biometricupdate.com/202010/lawsuit-alleges-biometric-privacy-violations-from-face-recognition-algorithm-training)\n* [Oct 2020 - You’re being watched: The dangers of ProctorU](http://udreview.com/youre-being-watched-the-dangers-of-proctoru/)\n* [Oct 2020 - Fake naked photos of thousands of women shared online](https://www.bbc.com/news/technology-54584127) \n* [Oct 2020 - Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers](https://venturebeat.com/2020/10/21/researchers-find-evidence-of-racial-gender-and-socioeconomic-bias-in-chest-x-ray-classifiers/)\n* [Oct 2020 - Uber sued by drivers over ‘automated robo-firing'](https://www.bbc.com/news/business-54698858)\n* [Oct 2020 - How an Algorithm Blocked Kidney Transplants to Black Patients](https://www.wired.com/story/how-algorithm-blocked-kidney-transplants-black-patients/)\n* [Oct 2020 - Australian researchers have shown how you could become invisible to security cameras](https://www.theaustralian.com.au/business/technology/australian-researchers-at-data61-show-you-could-become-invisible-to-a-security-camera/news-story/491b70e05c8fbdd566c1b2fd30b6d427)\n* [Oct 2020 - EPIC files lawsuit to force release of ICE facial recognition documents](https://www.biometricupdate.com/202010/epic-files-lawsuit-to-force-release-of-ice-facial-recognition-documents)\n* [Oct 2020 - Researchers take a stand on algorithm design for job centers: Landing a job isn't always the right goal](https://www.sciencedaily.com/releases/2020/10/201029105001.htm)\n* [Oct 2020 - Facebook under fire for boosting right-wing news sources and throttling progressive alternatives](https://www.salon.com/2020/10/29/facebook-under-fire-for-boosting-right-wing-news-sources-and-throttling-progressive-alternatives/)\n* [Oct 2020 - AI Camera Ruins Soccer Game For Fans After Mistaking Referee's Bald Head For Ball](https://www.iflscience.com/technology/ai-camera-ruins-soccar-game-for-fans-after-mistaking-referees-bald-head-for-ball/)\n* [Oct 2020 - Researchers made an OpenAI GPT-3 medical chatbot as an experiment. It told a mock patient to kill themselves](https://www.theregister.com/2020/10/28/gpt3_medical_chatbot_experiment/)\n* [Oct 2020 - Top doctors slam Google for not backing up incredible claims of super-human cancer-spotting AI](https://www.theregister.com/2020/10/16/google_ai_research/)\n* [Nov 2020 - Researchers show that computer vision algorithms pretrained on ImageNet exhibit multiple, distressing biases](https://venturebeat.com/2020/11/03/researchers-show-that-computer-vision-algorithms-pretrained-on-imagenet-exhibit-multiple-distressing-biases/)\n* [Nov 2020 - Trivago loses appeal over misleading website algorithm ruling](https://www.zdnet.com/article/trivago-loses-appeal-over-misleading-website-algorithm-ruling/)\n* [Nov 2020 - Research finds gender bias within state funding model](https://kobi5.com/news/local-news/research-finds-gender-bias-within-state-funding-model-140286/)\n* [Nov 2020 - Split-Second 'Phantom' Images Can Fool Tesla's Autopilot](https://www.wired.com/story/tesla-model-x-autopilot-phantom-images/)\n* [Nov 2020 - Boris executes U-turn over controversial house building algorithm](https://thenegotiator.co.uk/boris-executes-u-turn-over-controversial-house-building-algorithm/)\n* [Nov 2020 - Top intel official warns of bias in military algorithms](https://www.c4isrnet.com/artificial-intelligence/2020/11/18/top-intel-official-warns-of-bias-in-military-algorithms/)\n* [Nov 2020 - Opinion: Artificial 'Intelligence': Unemployment system denied legitimate COVID-19 claims](https://www.detroitnews.com/story/opinion/2020/11/19/opinion-unemployment-system-denied-legitimate-covid-19-claims/6339115002/)\n* [Nov 2020 - LAPD ban facial recognition following alleged unauthorised use](https://iottechnews.com/news/2020/nov/19/lapd-ban-facial-recognition-unauthorised-use/)\n* [Nov 2020 - Instagram removed 80 PER CENT less graphic content about suicide during the first three months of lockdown after 'most of its moderators were sent home due to Covid rules'](https://www.dailymail.co.uk/news/article-8969151/Instagram-removed-80-CENT-graphic-content-suicide.html)\n* [Nov 2020 - \t\nFacebook's AI Mistakenly Bans Ads for Struggling Businesses](https://www.bloomberg.com/news/articles/2020-11-27/facebook-s-ai-mistakenly-bans-ads-for-struggling-businesses)\n* [Nov 2020 - A Bot Made Frank Sinatra Cover Britney Spears. YouTube Removed It Over Copyright Claims.](https://futurism.com/bot-frank-sinatra-britney-spears-youtube-copyright)\n* [Nov 2020 - Net exposure \"94-year-old man was picked up for facial recognition\" The bank involved apologized](https://m.news.cctv.com/2020/11/23/ARTI4quWfQGGMIdgx5jojaaj201123.shtml)\n* [Nov 2020 - Walmart Scraps Plan to Have Robots Scan Shelves](https://www.wsj.com/articles/walmart-shelves-plan-to-have-robots-scan-shelves-11604345341)\n* [Dec 2020 - Concern over potential gender bias in job recruitment algorithms](https://www.abc.net.au/news/2020-12-02/potential-gender-bias-in-job-recruitment-application-algorithms/12943832?nw=0)\n* [Dec 2020 - Facial Recognition Company Lied to School District About its Racist Tech](https://www.vice.com/en/article/qjpkmx/fac-recognition-company-lied-to-school-district-about-its-racist-tech)\n* [Dec 2020 - China’s Huawei tested A.I. software that could identify Uighur Muslims and alert police, report says](https://www.cnbc.com/2020/12/09/chinas-huawei-tested-ai-software-that-could-identify-uighurs-report.html)\n* [Dec 2020 - We’ve Known Brand Safety Tech Was Bad—Here’s How Bad](https://www.forbes.com/sites/augustinefou/2020/12/06/weve-known-brand-safety-tech-was-bad-this-is-how-badly-it-defunds-the-news)\n* [Dec 2020 - Hey Alexa, what's my PIN?](https://www.dailymail.co.uk/sciencetech/article-9029811/Hey-Alexa-whats-PIN-Voice-assistants-figure-taps-smartphone-keyboard.html)\n* [Dec 2020 - Waze sent commuters toward California wildfires, drivers say](https://www.usatoday.com/story/tech/news/2017/12/07/california-fires-navigation-apps-like-waze-sent-commuters-into-flames-drivers/930904001/)\n* [Dec 2020 - The Death and Life of an Admissions Algorithm](https://www.insidehighered.com/admissions/article/2020/12/14/u-texas-will-stop-using-controversial-algorithm-evaluate-phd)\n* [Dec 2020 - Algorithms searching for child abuse could be banned under new EU privacy rules](https://www.telegraph.co.uk/technology/2020/12/20/algorithms-searching-child-abuse-could-banned-new-eu-privacy/)\n* [Dec 2020 - Alibaba ‘dismayed’ by its cloud unit’s ethnicity detection algorithm](https://techcrunch.com/2020/12/17/alibaba-ethnic-minority-algorithm/)\n* [Dec 2020 - Congress wants answers from Google about Timnit Gebru’s firing](https://www.technologyreview.com/2020/12/17/1014994/congress-wants-answers-from-google-about-timnit-gebrus-firing/)\n* [Dec 2020 - California Bar Exam Flagged A THIRD Of Applicants As Cheating](https://abovethelaw.com/2020/12/california-bar-exam-flagged-a-third-of-applicants-as-cheating/?rf=1)\n* [Dec 2020 - TikTok videos that promote anorexia are misspelling common hashtags to beat the 'pro-ana' ban](https://www.insider.com/tiktok-bans-six-accounts-posting-eating-disorder-content)\n* [Dec 2020 - Facial Recognition Blamed For False Arrest And Jail Time](https://www.silicon.co.uk/e-regulation/facial-recognition-false-arrest-349782?cmpredirect)\n* [Dec 2020 - Girl, 12, is suing social media giant TikTok for alleged misuse of personal information and breaches of data protection laws](https://www.dailymail.co.uk/news/article-9100755/Girl-12-suing-TikTok-alleged-misuse-personal-information-data-protection-law-breaches.html)\n* [Dec 2020 - TikTok Deleted My Account Because I’m a Latina Trans Woman](https://www.losangelesblade.com/2020/12/15/tiktok-deleted-my-account-because-im-a-latina-trans-woman/)\n* [Dec 2020 - Shopping mall robot fell off the escalator and knocked down passengers](https://s.weibo.com/weibo?q=%23%E5%95%86%E5%9C%BA%E6%9C%BA%E5%99%A8%E4%BA%BA%E6%8E%89%E4%B8%8B%E6%89%B6%E6%A2%AF%E6%92%9E%E5%80%92%E4%B9%98%E5%AE%A2%23&from=default)\n* [Dec 2020 - Stanford apologizes for coronavirus vaccine plan that left out many front-line doctors](https://www.washingtonpost.com/health/2020/12/18/stanford-hospital-protest-covid-vaccine/)\n* [Dec 2020 - The Christchurch Shooter and YouTube’s Radicalization Trap](https://www.wired.com/story/christchurch-shooter-youtube-radicalization-extremism/)\n* [Jan 2021 - Italian court rules against ‘discriminatory’ Deliveroo rider-ranking algorithm](https://techcrunch.com/2021/01/04/italian-court-rules-against-discriminatory-deliveroo-rider-ranking-algorithm/)\n* [Jan 2021 - A business owner who spent nearly $46 million on Facebook advertising says he's been booted from the platform without explanation](https://www.businessinsider.com/facebook-removed-shared-ceo-spent-46-million-on-ads-2021-1)\n* [Jan 2021 - FTC Orders Photo App to Delete Algorithms Built on Personal Data](https://epic.org/2021/01/ftc-orders-photo-app-to-delete.html)\n* [Jan 2021 - South Korean AI chatbot pulled from Facebook after hate speech towards minorities](https://www.theguardian.com/world/2021/jan/14/time-to-properly-socialise-hate-speech-ai-chatbot-pulled-from-facebook)\n* [Jan 2021 - Google Hit With $2B Antitrust Suit Over 'Rigging' Its Algorithm](https://www.law360.com/media/articles/1343696/google-hit-with-2b-antitrust-suit-over-rigging-its-algorithm)\n* [Jan 2021 - Judge Orders NJ Education Department To Turn Over S2 Algorithm](https://patch.com/new-jersey/brick/judge-orders-nj-education-department-turn-over-s2-algorithm)\n* [Jan 2021 - When an Israeli Farmer Declared War on an Algorithm](https://www.haaretz.com/israel-news/tech-news/.premium-when-an-israeli-farmer-declared-war-on-an-algorithm-1.9440728)\n* [Jan 2021 - Job Screening Service Halts Facial Analysis of Applicants](https://www.wired.com/story/job-screening-service-halts-facial-analysis-applicants/)\n* [Jan 2021 - Use of facial recognition tech sparks privacy fears](https://www.livemint.com/technology/tech-news/use-of-facial-recognition-tech-sparks-privacy-fears-11611536778871.html)\n* [Jan 2021 - South Korea has used AI to bring a dead superstar's voice back to the stage, but ethical concerns abound](https://www.kmov.com/south-korea-has-used-ai-to-bring-a-dead-superstars-voice-back-to-the-stage/article_f9df111e-b879-5c9e-80c9-aec19cbedc28.html?block_id=985917)\n* [Jan 2021 - SEC Orders BlueCrest to Pay $170 Million to Harmed Fund Investors](https://www.sec.gov/news/press-release/2020-308)\n* [Jan 2021 - University of Illinois to Discontinue Remote-Testing Software After Students Complain of Privacy Violation](https://www.techtimes.com/articles/256488/20210129/university-illinois-discontinue-remote-testing-software-students-complain-privacy-violation.htm)\n* [Jan 2021 - Amazon algorithms boost vaccine misinformation, says study](https://www.iol.co.za/technology/fintech/amazon-algorithms-boost-vaccine-misinformation-says-study-dc2105b8-dc86-4392-bc55-6555fe1dc77e)\n* [Jan 2021 - Patent applications listing AI as an inventor run into legal problems](https://www.chemistryworld.com/news/patent-applications-listing-ai-as-an-inventor-run-into-legal-problems/4013138.article)\n* [Jan 2021 - BIPOC students face disadvantages with exam monitoring software at the University of Toronto](https://thestrand.ca/bipoc-students-face-disadvantages-with-exam-monitoring-software-at-the-university-of-toronto/)\n* [Jan 2021 - ‘for Some Reason I’m Covered in Blood’: Gpt-3 Contains Disturbing Bias Against Muslims](https://onezero.medium.com/for-some-reason-im-covered-in-blood-gpt-3-contains-disturbing-bias-against-muslims-693d275552bf)\n* [Feb 2021 - Utah audit of Banjo deal highlights concerns with AI, government contracts](https://www.ksl.com/article/50099679/utah-audit-of-banjo-deal-highlights-concerns-with-large-government-tech-agreements)\n* [Feb 2021 - Lingerie company Adore Me calls out TikTok for removing videos of Black, plus-size models](https://www.usatoday.com/story/tech/2021/02/05/tiktok-slammed-removing-videos-adore-me-black-plus-size-models/4402625001/)\n* [Feb 2021 - ‘Orwellian’ AI lie detector project challenged in EU court](https://techcrunch.com/2021/02/05/orwellian-ai-lie-detector-project-challenged-in-eu-court)\n* [Feb 2021 - Clearview AI’s facial recognition technology violated federal and regional laws – RCI](https://thedailyguardian.net/clearview-ais-facial-recognition-technology-violated-federal-and-regional-laws-rci/)\n* [Feb 2021 - Beverly Hills cops try to weaponize Instagram’s algorithms in failed attempt to thwart live streamers](https://thenextweb.com/neural/2021/02/09/beverly-hills-cops-try-to-weaponize-instagrams-algorithms-in-failed-attempt-to-thwart-live-streamers/)\n* [Feb 2021 - AI displays bias and inflexibility in civility detection, study finds](https://venturebeat.com/2021/02/10/ai-displays-bias-and-inflexibility-in-civility-detection-study-finds/)\n* [Feb 2021 - Why Is Facebook Rejecting These Fashion Ads?](https://www.nytimes.com/2021/02/11/style/disabled-fashion-facebook-discrimination.html)\n* [Feb 2021 - Sweden’s data watchdog slaps police for unlawful use of Clearview AI](https://techcrunch.com/2021/02/12/swedens-data-watchdog-slaps-police-for-unlawful-use-of-clearview-ai/)\n* [Feb 2021 - AI-Wielding Hackers are Here](https://www.datacenterknowledge.com/security/ai-wielding-hackers-are-here)\n* [Feb 2021 - How Google Scholar Sidelines Research in Non‑English Languages](https://theswaddle.com/how-google-scholar-sidelines-research-in-non-english-languages/)\n* [Feb 2021 - DWP uses excessive surveillance on suspected fraudsters, report finds](https://www.theguardian.com/society/2021/feb/14/dwp-excessive-surveillance-on-suspected-fraudsters-privacy-international)\n* [Feb 2021 - Canada Rules Clearview’s AI Scraping is Unlawful](https://www.hstoday.us/industry/canada-rules-clearviews-ai-scraping-is-unlawful/)\n* [Feb 2021 - INVESTIGATION: Facebook, Twitter Struggling in Fight against Balkan Content Violations](https://balkaninsight.com/2021/02/16/facebook-twitter-struggling-in-fight-against-balkan-content-violations/)\n* [Feb 2021 - Google slapped in France over misleading hotel star ratings](https://techcrunch.com/2021/02/15/google-slapped-in-france-over-misleading-hotel-star-ratings/)\n* [Feb 2021 - Colleagues of mine analyzed A.I.-based job interviews ...](https://twitter.com/hatr/status/1362129235297660929) (Tweet)\n* [Feb 2021 - YouTuber blocked for discussing 'black versus white' chess strategy](https://www.dailymail.co.uk/sciencetech/article-9279473/YouTube-algorithm-accidentally-blocked-chess-player-discussing-black-versus-white-strategy.html)\n* [Feb 2021 - Teaneck just banned facial recognition technology for police. Here's why](https://www.northjersey.com/story/news/bergen/teaneck/2021/02/25/teaneck-nj-bans-facial-recognition-usage-police-citing-bias/6802839002/)\n* [Feb 2021 - TikTok agrees to pay $92 million to settle teen privacy class-action lawsuit](https://www.zdnet.com/article/tiktok-agrees-to-pay-92-million-to-settle-teen-privacy-class-action-lawsuit/)\n* [Feb 2021 - Google fires top ethical AI expert Margaret Mitchell](https://www.zdnet.com/article/google-fires-top-ethical-ai-expert-margaret-mitchell/)\n* [Mar 2021 - UP Uses Facial Recognition Technology to Mete Out Discriminatory Treatment](https://www.theleaflet.in/up-uses-facial-recognition-technology-to-mete-out-discriminatory-treatment/#)\n* [Mar 2021 - Chatbots that resurrect the dead: legal experts weigh in on ‘disturbing’ technology](https://theconversation.com/chatbots-that-resurrect-the-dead-legal-experts-weigh-in-on-disturbing-technology-155436)\n* [Mar 2021 - “It’s all the real thing,” Tom Cruise insists, looking into the camera ...](https://twitter.com/thetimes/status/1366442334544658432)\n* [Mar 2021 - OpenAI’s state-of-the-art machine vision AI is fooled by handwritten notes](https://www.theverge.com/2021/3/8/22319173/openai-machine-vision-adversarial-typographic-attacka-clip-multimodal-neuron)\n* [Mar 2021 - Major Universities are Using Race as a “High Impact Predictor” of Student Success – The Markup\n](https://themarkup.org/news/2021/03/02/major-universities-are-using-race-as-a-high-impact-predictor-of-student-success)\n* [Mar 2021 - Instagram Suggested Posts To Users. It Served Up COVID-19 Falsehoods, Study Finds](https://www.npr.org/2021/03/09/975032249/instagram-suggested-posts-to-users-it-served-up-covid-19-falsehoods-study-finds)\n* [Mar 2021 - Tenant screening software faces national reckoning](https://www.nbcnews.com/tech/tech-news/tenant-screening-software-faces-national-reckoning-n1260975)\n* [Mar 2021 - Instagram algorithm recommends far-right parties and Covid conspiracy theories to users](https://www.thetimes.co.uk/article/instagram-algorithm-recommends-far-right-parties-and-covid-conspiracy-theories-to-users-qjthq2xtg)\n* [Mar 2021 - Google image search cements national stereotypes of 'racy' women](https://www.dw.com/en/google-image-search-cements-national-stereotypes-of-racy-women/a-56767605)\n* [Mar 2021 - Time-Out for Google](https://www.insidehighered.com/news/2021/03/09/tech-transparency-conference-suspends-google-sponsorship-over-transparency-concerns)\n* [Mar 2021 - Apple Censors URLs Containing “Asian” with Adult Filters](https://www.mcgilldaily.com/2021/03/apple-censors-urls-containing-asian-with-adult-filters/)\n* [Mar 2021 - Underpaid Workers Are Being Forced to Train Biased AI on Mechanical Turk](https://www.vice.com/en/article/88apnv/underpaid-workers-are-being-forced-to-train-biased-ai-on-mechanical-turk)\n* [Mar 2021 - New Study Reveals Coded Language Used to Fuel Anti-Semitism Online](https://thejewishvoice.com/2021/03/new-study-reveals-coded-language-used-to-fuel-anti-semitism-online/)\n* [Mar 2021 - Judge tells state to deliver records](https://www.arkansasonline.com/news/2021/mar/04/judge-tells-state-deliver-records/)\n* [Mar 2021 - Pennsylvania Woman Accused of Using Deepfake Technology to Harass Cheerleaders](https://www.nytimes.com/2021/03/14/us/raffaela-spone-victory-vipers-deepfake.html)\n* [Mar 2021 - Fears of 'digital dictatorship' as Myanmar deploys artificial intelligence](https://www.straitstimes.com/asia/se-asia/fears-of-digital-dictatorship-as-myanmar-deploys-artificial-intelligence)\n* [Mar 2021 - Amazon driver quits, saying the final straw was the company's new AI-powered truck cameras that can sense when workers yawn or don't use a seatbelt](https://news.yahoo.com/amazon-driver-quits-saying-final-164140625.html)\n* [Mar 2021 - INSTA-KID Fury over Facebook plot to make NEW Instagram for under 13s – as parents brand it ‘dangerous’](https://www.thesun.co.uk/tech/14389470/instagram-for-kids-under-13-plans/)\n* [Mar 2021 - How AI lets bigots and trolls flourish while censoring LGBTQ+ voices](https://www.mic.com/p/how-ai-lets-bigots-trolls-flourish-while-censoring-lgbtq-voices-66661864)\n* [Mar 2021 - Music recommendation algorithms are unfair to female artists, but we can change that](https://theconversation.com/music-recommendation-algorithms-are-unfair-to-female-artists-but-we-can-change-that-158016)\n* [Mar 2021 - Couriers say Uber’s ‘racist’ facial identification tech got them fired](https://www.wired.co.uk/article/uber-eats-couriers-facial-recognition)\n* [Mar 2021 - Major flaws found in machine learning for COVID-19 diagnosis](https://venturebeat.com/2021/03/23/major-flaws-found-in-machine-learning-for-covid-19-diagnosis/)\n* [Mar 2021 - How a Stabbing in Israel Echoes Through the Fight Over Online Speech](https://www.nytimes.com/2021/03/24/technology/section-230-hearing-facebook.html)\n* [Apr 2021 - Researchers have found that even the best Speech recognition systems are actually biased](https://www.digitalinformationworld.com/2021/04/researchers-have-found-that-even-best.html)\n* [Apr 2021 - Research says Facebook's ad algorithm perpetuates gender bias](https://theintercept.com/2021/04/09/facebook-algorithm-gender-discrimination/) (see also [Research Outputs from Auditing for Discrimination in Job Ad Delivery](https://ant.isi.edu/datasets/addelivery/) on the USC Information Sciences Institute web site)\n* [Apr 2021 - Google AI chief Samy Bengio resigns over colleagues' firing and racial discrimination](https://www.wionews.com/technology/google-ai-chief-samy-bengio-resigns-over-colleagues-firing-and-racial-discrimination-375828)\n* [Apr 2021 - How medicine discriminates against non-white people and women](https://www.economist.com/science-and-technology/2021/04/08/how-medicine-discriminates-against-non-white-people-and-women)\n* [Apr 2021 - In scramble to respond to Covid-19, hospitals turned to models with high risk of bias](https://medcitynews.com/2021/04/in-scramble-to-respond-to-covid-19-hospitals-turned-to-models-with-high-risk-of-bias/)\n* [Apr 2021 - Home Office algorithm to detect sham marriages may contain built-in discrimination](https://www.thebureauinvestigates.com/stories/2021-04-19/home-office-algorithm-sham-marriages)\n* [Apr 2021 - Google translation AI botches legal terms 'enjoin,' 'garnish' -research](https://www.reuters.com/technology/google-translation-ai-botches-legal-terms-enjoin-garnish-research-2021-04-19/)\n* [Apr 2021 - Some FDA-approved AI medical devices are not ‘adequately’ evaluated, Stanford study says](https://venturebeat.com/2021/04/12/some-fda-approved-ai-medical-devices-are-not-adequately-evaluated-stanford-study-says/)\n* [Apr 2021 - Instagram apologises for mistake which targeted users with harmful diet content](https://www.harpersbazaar.com/uk/culture/culture-news/a36128394/instagram-harmful-diet-content/)\n* [Apr 2021 - Facebook, Princeton Must Face AI Data Theft Claims](https://www.law360.com/ip/articles/1375537/facebook-princeton-must-face-ai-data-theft-claims)\n* [Apr 2021 - Facebook sued for failing to remove anti-Muslim hate speech](https://www.thehindu.com/sci-tech/technology/internet/facebook-sued-for-failing-to-remove-anti-muslim-hate-speech/article34281168.ece)\n* [Apr 2021 - Post Office scandal: What the Horizon saga is all about](https://www.bbc.com/news/business-56718036)\n* [Apr 2021 - Facebook, Twitter, YouTube are pressed on ‘poisonous’ algorithms](https://www.latimes.com/business/technology/story/2021-04-27/facebook-twitter-youtube-pressed-on-poisonous-algorithms)\n* [Apr 2021 - BLACK MAN USES PASSPORT PHOTO AS EVIDENCE AI IS ‘RACIST’ IN VIRAL TIKTOK](https://www.independent.co.uk/life-style/ai-racist-robots-algorithm-tiktok-b1838521.html)\n* [Apr 2021 - Twitter allows ‘Uncle Tim’ to trend for hours after Sen. Tim Scott’s rebuttal, and then took action](https://nypost.com/2021/04/29/sen-tim-scott-attacked-as-uncle-tim-on-twitter-after-gop-rebuttal/)\n* [Apr 2021 - Suicide Risk Prediction Models Could Perpetuate Racial Disparities](https://healthitanalytics.com/news/suicide-risk-prediction-models-could-perpetuate-racial-disparities)\n* [May 2021 - Amsterdam Court orders reinstatement of Uber drivers dismissed by algorithm](https://ukhumanrightsblog.com/2021/05/18/amsterdam-court-orders-reinstatement-of-uber-drivers-dismissed-by-algorithm/)\n* [May 2021 - This facial recognition website can turn anyone into a cop — or a stalker](https://www.washingtonpost.com/technology/2021/05/14/pimeyes-facial-recognition-search-secrecy/)\n* [May 2021 - Why you should be very wary of AI that ‘processes’ college video applications](https://thenextweb.com/news/why-you-should-be-very-wary-of-ai-that-processes-college-video-applications)\n* [May 2021 - Airbnb pricing algorithm led to increased racial disparities, study finds](https://www.ft.com/content/5b1471e0-ed4a-47f5-8f3f-0a1ee7f7999c)\n* [May 2021 - Uber commits crime using algorithms](https://www.newframe.com/uber-commits-crime-using-algorithms/).\n* [May 2021 - Deepfake detectors and datasets exhibit racial and gender bias, USC study shows](https://venturebeat.com/2021/05/06/deepfake-detectors-and-datasets-exhibit-racial-and-gender-bias-usc-study-shows/)\n* [May 2021 - TikTok’s recommendation algorithm is promoting homophobia and anti-trans violence](https://www.losangelesblade.com/2021/05/18/tiktoks-recommendation-algorithm-is-promoting-homophobia-and-anti-trans-violence/)\n* [May 2021 - ‘Grassroots’ bot campaigns are coming. Governments don’t have a plan to stop them](https://www.washingtonpost.com/outlook/2021/05/20/ai-bots-grassroots-astroturf/)\n* [May 2021 - Workplace and algorithm bias kill Palestine content on Facebook and Twitter](https://www.trtworld.com/magazine/workplace-and-algorithm-bias-kill-palestine-content-on-facebook-and-twitter-46842) \n* [May 2021 - Suit seeks to limit anti-Muslim speech on Facebook but roots of Islamophobia run far deeper](https://theconversation.com/suit-seeks-to-limit-anti-muslim-speech-on-facebook-but-roots-of-islamophobia-run-far-deeper-159418)\n* [May 2021 - AI emotion-detection software tested on Uyghurs](https://www.bbc.com/news/technology-57101248)\n* [May 2021 - An Insurance Startup Bragged It Uses AI to Detect Fraud. It Didn’t Go Well](https://www.vice.com/en/article/z3x47y/an-insurance-startup-bragged-it-uses-ai-to-detect-fraud-it-didnt-go-well)\n* [May 2021 - Google's new AI skincare tool may not work on patients with darker skin tones](https://www.euronews.com/2021/05/26/google-s-new-ai-skincare-tool-may-not-work-on-patients-with-darker-skin-tones)\n* [May 2021 - Minn. Police Use of Facial Recognition Leads to Concerns](https://www.govtech.com/public-safety/minn-police-use-of-facial-recognition-leads-to-concerns)\n* [May 2021 - Facial recognition: Legal complaints lodged against Clearview AI in five countries](https://www.computing.co.uk/news/4032109/facial-recognition-legal-complaints-lodged-clearview-ai-countries)\n* [Jun 2021 - A Military Drone With A Mind Of Its Own Was Used In Combat, U.N. Says](https://www.npr.org/2021/06/01/1002196245/a-u-n-report-suggests-libya-saw-the-first-battlefield-killing-by-an-autonomous-d)\n* [Jun 2021 - Senate Democrats Urge Google To Investigate Racial Bias In Its Tools And The Company](https://www.npr.org/2021/06/02/1002525048/senate-democrats-to-google-investigate-racial-bias-in-your-tools-and-company)\n* [Jun 2021 - McDonald’s Taking Voiceprints at Drive-Throughs Illinois BIPA Class Action](https://classactionsreporter.com/mcdonalds-taking-voiceprints-at-drive-throughs-illinois-bipa-class-action/)\n* [Jun 2021 - Legal notice to Hyderabad Police Commissioner highlights lack of lawfulness of facial recognition measures](https://www.medianama.com/2021/06/223-hyderabad-police-facial-recognition-surveillance-masood/)\n* [Jun 2021 - ATER ALERT: The Klein Law Firm Announces a Lead Plaintiff Deadline of July 12, 2021 in the Class Action Filed on Behalf of Aterian, Inc. Limited Shareholders](https://finance.yahoo.com/news/ater-alert-klein-law-firm-002300149.html)\n* [Jun 2021 - Have Google’s Algorithm Updates Broken the Web?](https://centralrecorder.com/have-googles-algorithm-updates-broken-the-web/)\n* [Jun 2021 - How Airbnb failed its own anti-discrimination team—and let racial disparities slip through the cracks](https://www.morningbrew.com/emerging-tech/stories/2021/06/15/airbnb-failed-antidiscrimination-teamand-let-racial-disparities-slip-cracks)\n* [Jun 2021 - Facial Recognition Failures Are Locking People Out of Unemployment Systems](https://www.vice.com/en/article/5dbywn/facial-recognition-failures-are-locking-people-out-of-unemployment-systems)\n"
  },
  {
    "path": "code-of-conduct.md",
    "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to making participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, gender identity and expression, level of experience,\nnationality, personal appearance, race, religion, or sexual identity and\norientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\nadvances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n  address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n  professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces\nwhen an individual is representing the project or its community. Examples of\nrepresenting a project or community include using an official project e-mail\naddress, posting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event. Representation of a project may be\nfurther defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at jpatrickhall@gmail.com or datherton@gmail.com. \nAll complaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/\n"
  },
  {
    "path": "contributing.md",
    "content": "# Contribution Guidelines\n\nPlease note that this project is released with a [Contributor Code of Conduct](code-of-conduct.md) and under a [CC0 1.0 Universal License](LICENSE). By participating in this project you agree to abide by those terms.\n\n## Table of Contents\n\n* [Adding to this list by pull request](#adding-to-this-list-by-pull-request)\n* [Updating your Pull Request](#updating-your-pull-request)\n\n## Adding to This List by Pull Request\n\nPlease ensure your pull request adheres to the following guidelines:\n\n* Search previous list entries before making a new one, as yours may be a duplicate.\n* Make sure the entry is useful before submitting. (That implies it is easily accessible, currently useful as software or a reference, and has undergone some type of professional review.)\n* Categories should have 10s - 100s of entries, not thousands. We track some papers in our [library](library.md), but are not necessarily seeking paper or preprint contributions at this time. (That's what arXiv is for, right?) \n* Make an individual pull request for each added entry.\n* Add the new entry in alphabetical order in the appropriate category.\n* Use [title-casing](http://titlecase.com) (AP style).\n* Use the following format: `* [Resource Name](link)`\n* New categories or improvements to the existing categorization are welcome.\n* Check your spelling and grammar.\n* Make sure your text editor is set to remove trailing whitespace.\n* The pull request and commit should have a *useful* title.\n* The body of your commit message should contain a link to the software or reference you are adding.\n\n## Updating your Pull Request\n\nSometimes, a maintainer of this list may ask you to edit your Pull Request before it is included. This is normally due to spelling errors or because your PR didn't match the awesome list guidelines or Code of Conduct.\n\n[Here](https://github.com/RichardLitt/knowledge/blob/master/github/amending-a-commit-guide.md) is a write up on how to change a Pull Request, and the different ways you can do that.\n"
  },
  {
    "path": "library.md",
    "content": "### Scholarly Literature on Responsible AI\n\n* [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://arxiv.org/pdf/1811.10104.pdf)\n* [A Comparative Study of Fairness-Enhancing Interventions in Machine Learning](https://arxiv.org/pdf/1802.04422.pdf)\n* [A Survey Of Methods For Explaining Black Box Models](https://arxiv.org/pdf/1802.01933.pdf)\n* [A Marauder’s Map of Security and Privacy in Machine Learning](https://arxiv.org/pdf/1811.01134.pdf)\n* [Challenges for Transparency](https://arxiv.org/pdf/1708.01870.pdf)\n* [Closing the AI Accountability Gap](https://arxiv.org/pdf/2001.00973.pdf)\n* [DQI: Measuring Data Quality in NLP](https://arxiv.org/pdf/2005.00816.pdf)\n* [Explaining by Removing: A Unified Framework for Model Explanation](https://arxiv.org/abs/2011.14878)\n* [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://arxiv.org/pdf/1806.00069.pdf)\n* [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://arxiv.org/abs/1902.01876v1)\n* [Interpretable Machine Learning: Definitions, Methods, and Applications](https://arxiv.org/abs/1901.04592)\n* [Limitations of Interpretable Machine Learning](https://compstat-lmu.github.io/iml_methods_limitations/)\n* [Machine Learning Explainability in Finance](https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2019/machine-learning-explainability-in-finance-an-application-to-default-risk-analysis)\n* [On the Art and Science of Machine Learning Explanations](https://arxiv.org/pdf/1810.02909.pdf)\n* [Please Stop Explaining Black Box Models for High-Stakes Decisions](https://arxiv.org/pdf/1811.10154.pdf)\n* [Software Engineering for Machine Learning: A Case Study](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/amershi-icse-2019_Software_Engineering_for_Machine_Learning.pdf)\n* [The Mythos of Model Interpretability](https://arxiv.org/pdf/1606.03490.pdf)\n* [Towards A Rigorous Science of Interpretable Machine Learning](https://arxiv.org/pdf/1702.08608.pdf)\n* [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://arxiv.org/pdf/2004.07213.pdf)\n* [The Security of Machine Learning](https://people.eecs.berkeley.edu/~adj/publications/paper-files/SecML-MLJ2010.pdf)\n* [Techniques for Interpretable Machine Learning](https://arxiv.org/pdf/1808.00033.pdf)\n* [Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda](https://dl.acm.org/citation.cfm?id=3174156)\n* [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://arxiv.org/pdf/2011.03395.pdf)\n"
  }
]