Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| 8 Principles of Responsible ML | | | |
| A Brief Overview of AI Governance for Responsible Machine Learning Systems | | | |
| Acceptable Use Policies for Foundation Models | 5 | 2 months ago | |
| Access Now, Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report | | | |
| Ada Lovelace Institute, Code and Conduct: How to Create Third-Party Auditing Regimes for AI Systems | | | |
| Adversarial ML Threat Matrix | 1,037 | over 1 year ago | |
| AI Governance Needs Sociotechnical Expertise: Why the Humanities and Social Sciences Are Critical to Government Efforts | | | |
| AI Verify | | | : |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / AI Verify |
| AI Verify Foundation | | | |
| AI Verify Foundation, Cataloguing LLM Evaluations | | | |
| AI Verify Foundation, Generative AI: Implications for Trust and Governance | | | |
| AI Verfiy Foundation, Model Governance Framework for Generative AI | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| AI Snake Oil | | | * |
| The Alan Turing Institute, AI Ethics and Governance in Practice | | | |
| The Alan Turing Institute, Responsible Data Stewardship in Practice | | | |
| AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models | | | |
| Andreessen Horowitz (a16z) AI Canon | | | |
| Anthropic's Responsible Scaling Policy | | | |
| AuditBoard: 5 AI Auditing Frameworks to Encourage Accountability | | | |
| Auditing machine learning algorithms: A white paper for public auditors | | | |
| AWS Data Privacy FAQ | | | |
| AWS Privacy Notice | | | |
| AWS, What is Data Governance? | | | |
| Berryville Institute of Machine Learning, Architectural Risk Analysis of Large Language Models (requires free account login) | | | |
| BIML Interactive Machine Learning Risk Framework | | | |
| Boston University AI Task Force Report on Generative AI in Education and Research | | | |
| Brendan Bycroft's LLM Visualization | | | |
| Brown University, How Can We Tackle AI-Fueled Misinformation and Disinformation in Public Health? | | | |
| Casey Flores, AIGP Study Guide | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Center for Security and Emerging Technology (CSET): |
| CSET's Harm Taxonomy for the AI Incident Database | 12 | 4 months ago | |
| CSET Publications | | | |
| Adding Structure to AI Harm: An Introduction to CSET's AI Harm Framework | | | |
| AI Incident Collection: An Observational Study of the Great AI Experiment | | | |
| Repurposing the Wheel: Lessons for AI Standards | | | |
| Translating AI Risk Management Into Practice | | | |
| Understanding AI Harms: An Overview | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Censius: AI Audit | | | |
| Center for AI and Digital Policy Reports | | | |
| Center for Democracy and Technology (CDT), AI Policy & Governance | | | |
| Center for Democracy and Technology (CDT), Applying Sociotechnical Approaches to AI Governance in Practice | | | |
| Center for Democracy and Technology (CDT), In Deep Trouble: Surfacing Tech-Powered Sexual Harassment in K-12 Schools | | | |
| CivAI, GenAI Toolkit for the NIST AI Risk Management Framework: Thinking Through the Risks of a GenAI Chatbot | | | |
| Coalition for Content Provenance and Authenticity (C2PA) | | | |
| Crowe LLP: Internal auditor's AI safety checklist | | | |
| Data Provenance Explorer | | | |
| Data & Society, AI Red-Teaming Is Not a One-Stop Solution to AI Harms: Recommendations for Using Red-Teaming for AI Accountability | | | |
| Dealing with Bias and Fairness in AI/ML/Data Science Systems | | | |
| Debugging Machine Learning Models (ICLR workshop proceedings) | | | |
| Decision Points in AI Governance | | | |
| Demos, AI – Trustworthy By Design: How to build trust in AI systems, the institutions that create them and the communities that use them | | | |
| Digital Policy Alert, The Anatomy of AI Rules: A systematic comparison of AI rules across the globe | | | |
| Distill | | | |
| Dominique Shelton Leipzig, Countries With Draft AI Legislation or Frameworks | | | |
| Ethical and social risks of harm from Language Models | | | |
| Ethics for people who work in tech | | | |
| EU Digital Partners, U.S. A.I. Laws: A State-by-State Study | | | |
| Evaluating LLMs is a minefield | | | |
| Fairly's Global AI Regulations Map | 21 | 8 months ago | |
| FATML Principles and Best Practices | | | |
| Federation of American Scientists, A NIST Foundation To Support The Agency’s AI Mandate | | | |
| Financial Industry Regulatory Authority (FINRA), Artificial Intelligence (AI) in the Securities Industry | | | |
| ForHumanity Body of Knowledge (BOK) | | | |
| The Foundation Model Transparency Index | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / The Foundation Model Transparency Index |
| Trustible, Model Transparency Ratings | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| From Principles to Practice: An interdisciplinary framework to operationalise AI ethics | | | |
| The Future Society | | | |
| Gage Repeatability and Reproducibility | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Google: |
| Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing | | | |
| The Data Cards Playbook | | | |
| Data governance in the cloud - part 1 - People and processes | | | |
| Data Governance in the Cloud - part 2 - Tools | | | |
| Evaluating social and ethical risks from generative AI | | | |
| Generative AI Prohibited Use Policy | | | |
| Perspectives on Issues in AI Governance | | | |
| Principles and best practices for data governance in the cloud | | | |
| Responsible AI Framework | | | |
| Responsible AI practices | | | |
| Testing and Debugging in Machine Learning | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| H2O.ai Algorithms | 1,477 | 5 months ago | |
| HackerOne Blog | | | |
| Haptic Networks: How to Perform an AI Audit for UK Organisations | | | |
| Hogan Lovells, The AI Act is coming: EU reaches political agreement on comprehensive regulation of artificial intelligence | | | |
| Hugging Face, The Landscape of ML Documentation Tools | | | |
| IAPP, Global AI Governance Law and Policy: Canada, EU, Singapore, UK and US | | | |
| ICT Institute: A checklist for auditing AI systems | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / IEEE: |
| IEEE P3119 Standard for the Procurement of Artificial Intelligence and Automated Decision Systems | | | |
| IEEE Std 1012-1998 Standard for Software Verification and Validation | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Independent Audit of AI Systems | | | |
| Identifying and Overcoming Common Data Mining Mistakes | | | |
| Infocomm Media Development Authority (Singapore) and AI Verify Foundation, Cataloguing LLM Evaluations, Draft for Discussion (October 2023) | | | |
| Infocomm Media Development Authority (Singapore), First of its kind Generative AI Evaluation Sandbox for Trusted AI by AI Verify Foundation and IMDA | | | |
| Institute for AI Policy and Strategy (IAPS), AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies | | | |
| Institute for Public Policy Research (IPPR), Transformed by AI: How Generative Artificial Intelligence Could Affect Work in the UK—And How to Manage It | | | |
| Institute of Internal Auditors: Artificial Intelligence Auditing Framework, Practical Applications, Part A, Special Edition | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / ISACA: |
| ISACA: Auditing Artificial Intelligence | | | |
| ISACA: Auditing Guidelines for Artificial Intelligence | | | |
| ISACA: Capability Maturity Model Integration Resources | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Integrity Institute Report, February 2024, On Risk Assessment and Mitigation for Algorithmic Systems | | | |
| ISO/IEC 42001:2023, Information technology — Artificial intelligence — Management system | | | |
| Know Your Data | | | |
| Language Model Risk Cards: Starter Set | 24 | 4 months ago | |
| Large language models, explained with a minimum of math and jargon | | | |
| Larry G. Wlosinski, April 30, 2021, Information System Contingency Planning Guidance | | | |
| Library of Congress, LC Labs AI Planning Framework | 26 | 5 months ago | |
| Llama 2 Responsible Use Guide | | | |
| LLM Visualization | | | |
| Machine Learning Quick Reference: Algorithms | | | |
| Machine Learning Quick Reference: Best Practices | | | |
| Manifest MLBOM Wiki | 27 | 8 months ago | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Manifest MLBOM Wiki |
| Towards Traceability in Data Ecosystems using a Bill of Materials Model | | | |
| |
| System cards | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Microsoft: |
| Advancing AI responsibly | | | |
| Azure AI Content Safety | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Microsoft: / Azure AI Content Safety |
| Harm categories in Azure AI Content Safety | | | |
| Microsoft Responsible AI Standard, v2 | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Microsoft: |
| GDPR and Generative AI: A Guide for Public Sector Organizations | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| MLA, How do I cite generative AI in MLA style? | | | |
| model-cards-and-datasheets | 70 | 4 months ago | |
| NewsGuard AI Tracking Center | | | |
| OpenAI, Building an early warning system for LLM-aided biological threat creation | | | |
| OpenAI Cookbook, How to implement LLM guardrails | | | |
| OpenAI, Evals | 14,720 | about 2 months ago | |
| Open Data Institute, Understanding data governance in AI: Mapping governance | | | |
| Open Sourcing Highly Capable Foundation Models | | | |
| Organization and Training of a Cyber Security Team | | | |
| Our Data Our Selves, Data Use Policy | | | |
| OWASP, Guide for Preparing and Responding to Deepfake Events: From the OWASP Top 10 for LLM Applications Team, Version 1, September 2024 | | | |
| Oxford Commission on AI & Good Governance, AI in the Public Service: From Principles to Practice | | | |
| PAIR Explorables: Datasets Have Worldviews | | | |
| Partnership on AI, ABOUT ML Reference Document | | | |
| Partnership on AI, PAI’s Guidance for Safe Foundation Model Deployment: A Framework for Collective Action | | | |
| Partnership on AI, Responsible Practices for Synthetic Media: A Framework for Collective Action | | | |
| PwC's Responsible AI | | | |
| RAND Corporation, U.S. Tort Liability for Large-Scale Artificial Intelligence Damages
A Primer for Developers and Policymakers | | | |
| RAND Corporation, Analyzing Harms from AI-Generated Images and Safeguarding Online Authenticity | | | |
| Ravit Dotan's Projects | | | |
| Real-World Strategies for Model Debugging | | | |
| RecoSense: Phases of an AI Data Audit – Assessing Opportunity in the Enterprise | | | |
| Robust ML | | | |
| Safe and Reliable Machine Learning | | | |
| Sample AI Incident Response Checklist | | | |
| Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet | | | |
| SHRM Generative Artificial Intelligence (AI) Chatbot Usage Policy | | | |
| Special Competitive Studies Project and Johns Hopkins University Applied Physics Laboratory, Framework for Identifying Highly Consequential AI Use Cases | | | |
| Stanford University, Open Problems in Technical AI Governance: A repository of open problems in technical AI governance | | | |
| Stanford University, Responsible AI at Stanford: Enabling innovation through AI best practices | | | |
| Synack, The Complete Guide to Crowdsourced Security Testing, Government Edition | | | |
| The Rise of Generative AI and the Coming Era of Social Media Manipulation 3.0: Next-Generation Chinese Astroturfing and Coping with Ubiquitous AI | | | |
| Taskade: AI Audit PBC Request Checklist Template | | | |
| Taylor & Francis, AI Policy | | | |
| Tech Policy Press - Artificial Intelligence | | | |
| TechTarget: 9 questions to ask when auditing your AI systems | | | |
| Troubleshooting Deep Neural Networks | | | |
| Trustible, Enhancing the Effectiveness of AI Governance Committees | | | |
| Twitter Algorithmic Bias Bounty | | | |
| Unite.AI: How to perform an AI Audit in 2023 | | | |
| University of California, Berkeley, Center for Long-Term Cybersecurity, A Taxonomy of Trustworthiness for Artificial Intelligence | | | |
| University of California, Berkeley, Information Security Office, How to Write an Effective Website Privacy Statement | | | |
| University of Washington Tech Policy Lab, Data Statements | | | |
| Warning Signs: The Future of Privacy and Security in an Age of Machine Learning | | | |
| When Not to Trust Your Explanations | | | |
| Why We Need to Know More: Exploring the State of AI Incident Documentation Practices | | | |
| WilmerHale, What Are High-Risk AI Systems Within the Meaning of the EU’s AI Act, and What Requirements Apply to Them? | | | |
| World Economic Forum, Responsible AI Playbook for Investors | | | |
| World Privacy Forum, AI Governance on the Ground: Canada’s Algorithmic Impact Assessment Process and Algorithm has evolved | | | |
| World Privacy Forum, Risky Analysis: Assessing and Improving AI Governance Tools | | | |
| You Created A Machine Learning Application Now Make Sure It's Secure | | | |
| A-LIGN, ISO 42001 Requirement, NIST SP 800-218A Task, Recommendations and Considerations | | | |
| AppliedAI Institute, Navigating the EU AI Act: A Process Map for making AI Systems available | | | |
| BCG Robotaxonomy | | | |
| Center for Security and Emerging Technology (CSET), High Level Comparison of Legislative Perspectives on Artificial Intelligence US vs. EU | | | |
| European Data Protection Board (EDPB), Checklist for AI Auditing | | | |
| Foundation Model Transparency Index Scores by Major Dimensions of Transparency, May 2024 | | | |
| Future of Privacy Forum, EU AI Act: A Comprehensive Implementation & Compliance Timeline | | | |
| Future of Privacy Forum, The Spectrum of Artificial Intelligence | | | |
| IAPP EU AI Act Cheat Sheet | | | |
| IAPP, EU AI Act Compliance Matrix | | | |
| IAPP, EU AI Act Compliance Matrix - At a Glance | | | |
| Instruction finetuning an LLM from scratch | | | |
| Machine Learning Attack_Cheat_Sheet | | | |
| Oliver Patel's Cheat Sheets | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Oliver Patel's Cheat Sheets |
| 10 Key Pillars for Enterprise AI Governance | | | |
| 10 Key Questions for AI Risk Assessments | | | |
| AI Governance in 2023 | | | |
| Canada AI Law & Policy Cheat Sheet | | | |
| China AI Law Cheat Sheet | | | |
| Definitions, Scope & Applicability EU AI Act Cheat Sheet Series, Part 1 | | | |
| EU AI Act Cheat Sheet Series 1, Definitions, Scope & Applicability | | | |
| EU AI Act Cheat Sheet Series 2, Prohibited AI Systems | | | |
| EU AI Act Cheat Sheet Series 3, High-Risk AI Systems | | | |
| EU AI Act Cheat Sheet Series 4, Requirements for Providers | | | |
| EU AI Act Cheat Sheet Series 5, Requirements for Deployers | | | |
| EU AI Act Cheat Sheet Series 6, General-Purpose AI Models | | | |
| EU AI Act Cheat Sheet Series 7, Compliance & Conformity Assessment | | | |
| EU AI Act Cheat Sheet Series 8, Governance & Enforcement | | | |
| India AI Policy Cheat Sheet | | | |
| Governance Audit, Model Audit, and Application Audit | | | |
| Gulf Countries AI Policy Cheat Sheet | | | |
| Singapore AI Policy Cheat Sheet | | | |
| UK AI Policy Cheat Sheet | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Open Source Audit Tooling (OAT) Landscape | | | |
| Phil Lee, AI Act: Difference between AI systems and AI models | | | |
| Phil Lee, AI Act: Meet the regulators! (Arts 30, 55b, 56 and 59) | | | |
| Phil Lee, How the AI Act applies to integrated generative AI | | | |
| Phil Lee, Overview of AI Act requirements for deployers of high risk AI systems | | | |
| Phil Lee, Overview of AI Act requirements for providers of high risk AI systems | | | |
| Purpose and Means AI Explainer Series - issue #4 - Navigating the EU AI Act | | | |
| Trustible, Is It AI? How different laws & frameworks define AI | | | |
| What Access Protections Do AI Companies Provide for Independent Safety Research? | | | |
| Exploiting Novel GPT-4 APIs | | | |
| Identifying and Eliminating CSAM in Generative ML Training Data and Models | | | |
| Jailbreaking Black Box Large Language Models in Twenty Queries | | | |
| LLM Agents can Autonomously Exploit One-day Vulnerabilities | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / LLM Agents can Autonomously Exploit One-day Vulnerabilities |
| No, LLM Agents can not Autonomously Exploit One-day Vulnerabilities | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned | | | |
| Red Teaming of Advanced Information Assurance Concepts | | | |
| @dotey on X/Twitter exploring GPT prompt security and prevention measures | | | |
| 0xeb / GPT-analyst | 178 | 7 months ago | |
| 0xk1h0 / ChatGPT "DAN" (and other "Jailbreaks") | 6,367 | about 2 months ago | |
| ACL 2024 Tutorial: Vulnerabilities of Large Language Models to Adversarial Attacks | | | |
| Azure's PyRIT | 1,735 | 14 days ago | |
| 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/ | | | |
| CDAO frameworks, guidance, and best practices for AI test & evaluation | | | |
| ChatGPT_system_prompt | 8,038 | 8 days ago | |
| coolaj86 / Chat GPT "DAN" (and other "Jailbreaks") | | | |
| CSET, What Does AI-Red Teaming Actually Mean? | | | |
| DAIR Prompt Engineering Guide | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / DAIR Prompt Engineering Guide |
| DAIR Prompt Engineering Guide GitHub | 47,912 | 16 days ago | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Extracting Training Data from ChatGPT | | | |
| Frontier Model Forum: What is Red Teaming? | | | |
| Generative AI Red Teaming Challenge: Transparency Report 2024 | | | |
| HackerOne, An Emerging Playbook for AI Red Teaming with HackerOne | | | |
| Humane Intelligence, SeedAI, and DEFCON AI Village, Generative AI Red Teaming Challenge: Transparency Report 2024 | | | |
| In-The-Wild Jailbreak Prompts on LLMs | 2,118 | 2 months ago | |
| leeky: Leakage/contamination testing for black box language models | 6 | 7 months ago | |
| LLM Security & Privacy | 404 | 27 days ago | |
| Membership Inference Attacks and Defenses on Machine Learning Models Literature | 272 | 4 months ago | |
| Learn Prompting, Prompt Hacking | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / Learn Prompting, Prompt Hacking |
| MiesnerJacob / learn-prompting, Prompt Hacking | 31 | over 1 year ago | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Lakera AI's Gandalf | | | |
| leondz / garak | 1,294 | 11 days ago | |
| Microsoft AI Red Team building future of safer AI | | | |
| OpenAI Red Teaming Network | | | |
| r/ChatGPTJailbreak | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance / r/ChatGPTJailbreak |
| developer mode fixed | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Community Frameworks and Guidance |
| Y Combinator, ChatGPT Grandma Exploit | | | |
| Backpack Language Models | | | |
| Jay Alammar, Finding the Words to Say: Hidden State Visualizations for Language Models | | | |
| Jay Alammar, Interfaces for Explaining Transformer Language Models | | | |
| Patrick Hall and Daniel Atherton, Generative AI Risk Management Resources | 8 | 4 days ago | |
| Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet | | | |
| The Remarkable Robustness of LLMs: Stages of Inference? | | | |
| Columbia Business School, Generative AI Policy | | | |
| Columbia University, Considerations for AI Tools in the Classroom | | | |
| Columbia University, Generative AI Policy | | | |
| Georgetown University, Artificial Intelligence and Homework Support Policies | | | |
| Georgetown University, Artificial Intelligence (Generative) Resources | | | |
| Georgetown University, Teaching with AI | | | |
| George Washington University, Faculty Resources: Generative AI | | | |
| George Washington University, Guidelines for Using Generative Artificial Intelligence at the George Washington University April 2023 | | | |
| George Washington University, Guidelines for Using Generative Artificial Intelligence in Connection with Academic Work | | | |
| Harvard Business School, 2.1.2 Using ChatGPT & Artificial Intelligence (AI) Tools | | | |
| Harvard Graduate School of Education, HGSE AI Policy | | | |
| Harvard University, AI Guidance & FAQs | | | |
| Harvard University, Guidelines for Using ChatGPT and other Generative AI tools at Harvard | | | |
| Massachusetts Institute of Technology, Initial guidance for use of Generative AI tools | | | |
| Massachusetts Institute of Technology, Generative AI & Your Course | | | |
| Stanford Graduate School of Business, Course Policies on Generative AI Use | | | |
| Stanford University, Artificial Intelligence Teaching Guide | | | |
| Stanford University, Creating your course policy on AI | | | |
| Stanford University, Generative AI Policy Guidance | | | |
| Stanford University, Responsible AI at Stanford | | | |
| University of California, AI Governance and Transparency | | | |
| University of California, Applicable Law and UC Policy | | | |
| University of California, Legal Alert: Artificial Intelligence Tools | | | |
| University of California, Berkeley, AI at UC Berkeley | | | |
| University of California, Berkeley, Appropriate Use of Generative AI Tools | | | |
| University of California, Irvine, Generative AI for Teaching and Learning | | | |
| University of California, Irvine, Statement on Generative AI Detection | | | |
| University of California, Los Angeles, Artificial Intelligence (A.I.) Tools and Academic Use | | | |
| University of California, Los Angeles, ChatGPT and AI Resources | | | |
| University of California, Los Angeles, Generative AI | | | |
| University of California, Los Angeles, Guiding Principles for Responsible Use | | | |
| University of California, Los Angeles, Teaching Guidance for ChatGPT and Related AI Developments | | | |
| University of Notre Dame, AI Recommendations for Instructors | | | |
| University of Notre Dame, AI@ND Policies and Guidelines | | | |
| University of Notre Dame, Generative AI Policy for Students | | | |
| University of Southern California, Using Generative AI in Research | | | |
| University of Washington, AI+Teaching | | | |
| University of Washington, AI+Teaching, Sample syllabus statements regarding student use of artificial intelligence | | | |
| Yale University, AI at Yale | | | |
| Yale University, AI Guidance for Teachers | | | |
| Yale University, Yale University AI guidelines for staff | | | |
| Yale University, Guidelines for the Use of Generative AI Tools | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops |
| AAAI Conference on Artificial Intelligence | | | |
| ACM FAccT (Fairness, Accountability, and Transparency) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops / ACM FAccT (Fairness, Accountability, and Transparency) |
| FAT/ML (Fairness, Accountability, and Transparency in Machine Learning) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops |
| ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) | | | |
| AIES (AAAI/ACM Conference on AI, Ethics, and Society) | | | |
| Black in AI | | | |
| Computer Vision and Pattern Recognition (CVPR) | | | |
| Evaluating Generative AI Systems: the Good, the Bad, and the Hype (April 15, 2024) | | | |
| IAPP, AI Governance Global 2024, June 4-7, 2024 | | | |
| International Conference on Machine Learning (ICML) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops / International Conference on Machine Learning (ICML) / : |
| 2nd ICML Workshop on Human in the Loop Learning (HILL) | | | |
| 5th ICML Workshop on Human Interpretability in Machine Learning (WHI) | | | |
| Challenges in Deploying and Monitoring Machine Learning Systems | | | |
| Economics of privacy and data labor | | | |
| Federated Learning for User Privacy and Data Confidentiality | | | |
| Healthcare Systems, Population Health, and the Role of Health-tech | | | |
| Law & Machine Learning | | | |
| ML Interpretability for Scientific Discovery | | | |
| MLRetrospectives: A Venue for Self-Reflection in ML Research | | | |
| Participatory Approaches to Machine Learning | | | |
| XXAI: Extending Explainable AI Beyond Deep Models and Classifiers | | | |
| Human-AI Collaboration in Sequential Decision-Making | | | |
| Machine Learning for Data: Automated Creation, Privacy, Bias | | | |
| ICML Workshop on Algorithmic Recourse | | | |
| ICML Workshop on Human in the Loop Learning (HILL) | | | |
| ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI | | | |
| Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3) | | | |
| International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21) | | | |
| Interpretable Machine Learning in Healthcare | | | |
| Self-Supervised Learning for Reasoning and Perception | | | |
| The Neglected Assumptions In Causal Inference | | | |
| Theory and Practice of Differential Privacy | | | |
| Uncertainty and Robustness in Deep Learning | | | |
| Workshop on Computational Approaches to Mental Health @ ICML 2021 | | | |
| Workshop on Distribution-Free Uncertainty Quantification | | | |
| Workshop on Socially Responsible Machine Learning | | | |
| 1st ICML 2022 Workshop on Safe Learning for Autonomous Driving (SL4AD) | | | |
| 2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH) | | | |
| DataPerf: Benchmarking Data for Data-Centric AI | | | |
| Disinformation Countermeasures and Machine Learning (DisCoML) | | | |
| Responsible Decision Making in Dynamic Environments | | | |
| Spurious correlations, Invariance, and Stability (SCIS) | | | |
| The 1st Workshop on Healthcare AI and COVID-19 | | | |
| Theory and Practice of Differential Privacy | | | |
| Workshop on Human-Machine Collaboration and Teaming | | | |
| 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning | | | |
| 2nd Workshop on Formal Verification of Machine Learning | | | |
| 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH) | | | |
| Challenges in Deployable Generative AI | | | |
| “Could it have been different?” Counterfactuals in Minds and Machines | | | |
| Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities | | | |
| Generative AI and Law (GenLaw) | | | |
| Interactive Learning with Implicit Human Feedback | | | |
| Neural Conversational AI Workshop - What’s left to TEACH (Trustworthy, Enhanced, Adaptable, Capable and Human-centric) chatbots? | | | |
| The Second Workshop on Spurious Correlations, Invariance and Stability | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops |
| Knowledge, Discovery, and Data Mining (KDD) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops / Knowledge, Discovery, and Data Mining (KDD) / : |
| 2nd ACM SIGKDD Workshop on Ethical Artificial Intelligence: Methods and Applications | | | |
| KDD Data Science for Social Good 2023 | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops |
| Mission Control AI, Booz Allen Hamilton, and The Intellectual Forum at Jesus College, Cambridge, The 2024 Leaders in Responsible AI Summit, March 22, 2024 | | | |
| NAACL 24 Tutorial: Explanations in the Era of Large Language Models | | | |
| Neural Information Processing Systems (NeurIPs) | | | |
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| 5th Robot Learning Workshop: Trustworthy Robotics | | | |
| Algorithmic Fairness through the Lens of Causality and Privacy | | | |
| Causal Machine Learning for Real-World Impact | | | |
| Challenges in Deploying and Monitoring Machine Learning Systems | | | |
| Cultures of AI and AI for Culture | | | |
| Empowering Communities: A Participatory Approach to AI for Mental Health | | | |
| Federated Learning: Recent Advances and New Challenges | | | |
| Gaze meets ML | | | |
| HCAI@NeurIPS 2022, Human Centered AI | | | |
| Human Evaluation of Generative Models | | | |
| Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022 | | | |
| I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification | | | |
| Learning Meaningful Representations of Life | | | |
| Machine Learning for Autonomous Driving | | | |
| Progress and Challenges in Building Trustworthy Embodied AI | | | |
| Tackling Climate Change with Machine Learning | | | |
| Trustworthy and Socially Responsible Machine Learning | | | |
| Workshop on Machine Learning Safety | | | |
| AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics | | | |
| Algorithmic Fairness through the Lens of Time | | | |
| Attributing Model Behavior at Scale (ATTRIB) | | | |
| Backdoors in Deep Learning: The Good, the Bad, and the Ugly | | | |
| Computational Sustainability: Promises and Pitfalls from Theory to Deployment | | | |
| I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models | | | |
| Socially Responsible Language Modelling Research (SoLaR) | | | |
| Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations | | | |
| Workshop on Distribution Shifts: New Frontiers with Foundation Models | | | |
| XAI in Action: Past, Present, and Future Applications | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Conferences and Workshops |
| OECD.AI, Building the foundations for collaboration: The OECD-African Union AI Dialogue | | | |
| Oxford Generative AI Summit Slides | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| Department of Industry, Science and Resources, AI Governance: Leadership insights and the Voluntary AI Safety Standard in practice | | | |
| Department of Industry, Science and Resources, Australia’s AI Ethics Principles | | | |
| Department of Industry, Science and Resources, Introducing mandatory guardrails for AI in high-risk settings: proposals paper | | | |
| Digital Transformation Agency, Policy for the responsible use of AI in government, September 2024, Version 1.1 | | | |
| National framework for the assurance of artificial intelligence in government | | | |
| Testing the Reliability, Validity, and Equity of Terrorism Risk Assessment Instruments | | | |
| A Regulatory Framework for AI: Recommendations for PIPEDA Reform | | | |
| Developing Financial Sector Resilience in a Digital World: Selected Themes in Technology and Related Risks | | | |
| Directive on Automated Decision Making (Canada) | | | |
| (Draft Guideline) E-23 – Model Risk Management | | | |
| Health Canada, Transparency for machine learning-enabled medical devices: Guiding principles | | | |
| Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (France) | | | |
| Bundesamt für Sicherheit in der Informationstechnik, Generative AI Models - Opportunities and Risks for Industry and Authorities | | | |
| Autoriteit Persoonsgegevens, scraping bijna altijd illegal (Dutch Data Protection Authority, "Scraping is always illegal") | | | |
| General principles for the use of Artificial Intelligence in the financial sector | | | |
| AI Safety Institute (AISI), Advanced AI evaluations at AISI: May update | | | |
| Algorithm Charter for Aotearoa New Zealand | | | |
| Callaghan Innovation, EU AI Fact Sheet 4, High-risk AI systems | | | |
| Personal Data Protection Commission (PDPC), Companion to the Model AI Governance Framework – Implementation and Self-Assessment Guide for Organizations | | | |
| Personal Data Protection Commission (PDPC), Compendium of Use Cases: Practical Illustrations of the Model AI Governance Framework | | | |
| Personal Data Protection Commission (PDPC), Model Artificial Intelligence Governance Framework (Second Edition) | | | |
| Personal Data Protection Commission (PDPC), Privacy Enhancing Technology (PET): Proposed Guide on Synthetic Data Generation | | | |
| AI Safety Institute (AISI), Safety cases at AISI | | | |
| Department for Science, Innovation and Technology and AI Safety Institute, International Scientific Report on the Safety of Advanced AI | | | |
| Department for Science, Innovation and Technology, The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023 | | | |
| Department for Science, Innovation and Technology, Frontier AI: capabilities and risks - discussion paper (United Kingdom) | | | |
| Department for Science, Innovation and Technology, Guidance, Introduction to AI Assurance | | | |
| National Physical Laboratory (NPL), Beginner's guide to measurement GPG118 | | | |
| Online Harms White Paper: Full government response to the consultation (United Kingdom) | | | |
| The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018 | | | |
| 12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B) | | | |
| Chatbots in consumer finance | | | |
| Innovation spotlight: Providing adverse action notices when using AI/ML models | | | |
| A Primer on Artificial Intelligence in Securities Markets | | | |
| Responsible Artificial Intelligence in Financial Markets | | | |
| Artificial Intelligence: Background, Selected Issues, and Policy Considerations, May 19, 2021 | | | |
| Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th Congress, August 4, 2023 | | | |
| Highlights of the 2023 Executive Order on Artificial Intelligence for Congress, November 17, 2023 | | | |
| Artificial Intelligence and Machine Learning in Financial Services, April 3, 2024 | | | |
| Copyright and Artificial Intelligence, Part 1: Digital Replicas, July 2024 | | | |
| Privacy Policy and Data Policy | | | |
| Explainable Artificial Intelligence (XAI) (Archived) | | | |
| Computer Security Technology Planning Study, October 1, 1972 | | | |
| Artificial intelligence | | | |
| Intellectual property | | | |
| National Institute of Standards and Technology (NIST) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / National Institute of Standards and Technology (NIST) |
| AI 100-1 Artificial Intelligence Risk Management Framework (NIST AI RMF 1.0) | | | |
| De-identification Tools | | | |
| NIST AI 100-2 E2023: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations | | | |
| Assessing Risks and Impacts of AI (ARIA) | | | |
| Four Principles of Explainable Artificial Intelligence, Draft NISTIR 8312, 2020-08-17 | | | |
| Four Principles of Explainable Artificial Intelligence, NISTIR 8312, 2021-09-29 | | | |
| Engineering Statistics Handbook | | | |
| Measurement Uncertainty | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / National Institute of Standards and Technology (NIST) / Measurement Uncertainty |
| International Bureau of Weights and Measures (BIPM), Evaluation of measurement data—Guide to the expression of uncertainty in measurement | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / National Institute of Standards and Technology (NIST) |
| NIST Special Publication 800-30 Revision 1, Guide for Conducting Risk Assessments | | | |
| Psychological Foundations of Explainability and Interpretability in Artificial Intelligence | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| National Telecommunications and Information Administration (NTIA) | | | |
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| AI Accountability Policy Report | | | |
| Internet Policy Task Force, Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy Framework | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| AI Principles: Recommendations on the Ethical Use of Artificial Intelligence | | | |
| Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology | | | |
| Chief Data and Artificial Intelligence Officer (CDAO) Assessment and Assurance | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Chief Data and Artificial Intelligence Officer (CDAO) Assessment and Assurance |
| RAI Toolkit | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Department of the Army |
| Proceedings of the Thirteenth Annual U.S. Army Operations Research Symposium, Volume 1, October 29 to November 1, 1974 | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| Guidelines for secure AI system development | | | |
| Office of Educational Technology | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Office of Educational Technology |
| Designing for Education with Artificial Intelligence: An Essential Guide for Developers | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| Artificial Intelligence and Technology Office | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Artificial Intelligence and Technology Office |
| AI Risk Management Playbook (AIRMP) | | | |
| AI Use Case Inventory (DOE Use Cases Releasable to Public in Accordance with E.O. 13960) | | | |
| Digital Climate Solutions Inventory | | | |
| Generative Artificial Intelligence Reference Guide | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| Artificial Intelligence and Autonomous Systems | | | |
| Artificial Intelligence Safety and Security Board | | | |
| Department of Homeland Security Artificial Intelligence Roadmap 2024 | | | |
| Safety and Security Guidelines for Critical Infrastructure Owners and Operators | | | |
| Use of Commercial Generative Artificial Intelligence (AI) Tools | | | |
| Artificial Intelligence Strategy for the U.S. Department of Justice, December 2020 | | | |
| Civil Rights Division, Artificial Intelligence and Civil Rights | | | |
| Privacy Act of 1974 | | | |
| Overview of The Privacy Act of 1974 (2020 Edition) | | | |
| Managing Artificial Intelligence-Specific Cybersecurity Risks in the Financial Services Sector, March 2024 | | | |
| EEOC Letter (from U.S. senators re: hiring software) | | | |
| Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures | | | |
| 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 | | | |
| Office of Management and Budget (OMB) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Office of Management and Budget (OMB) |
| Guidance for Regulation of Artificial Intelligence Applications, November 2020 | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| Office of Science and Technology Policy (OSTP) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Office of Science and Technology Policy (OSTP) |
| Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People | | | |
| National Science and Technology Council (NSTC) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Office of Science and Technology Policy (OSTP) / National Science and Technology Council (NSTC) |
| Select Committee on Artificial Intelligence, National Artificial Intelligence Research and Development Strategic Plan 2023 Update | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| Supervisory Guidance on Model Risk Management | | | |
| Advisory Bulletin AB 2013-07 Model Risk Management Guidance | | | |
| Supervisory Guidance on Model Risk Management | | | |
| Artificial Intelligence/Machine Learning (AI/ML)-Based: Software as a Medical Device (SaMD) Action Plan, updated January 2021 | | | |
| Business Blog | | | : |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Business Blog |
| 2020-04-08 Using Artificial Intelligence and Algorithms | | | |
| 2021-01-11 Facing the facts about facial recognition | | | |
| 2021-04-19 Aiming for truth, fairness, and equity in your company’s use of AI | | | |
| 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 | | | |
| 2023-07-25 Protecting the privacy of health information: A baker’s dozen takeaways from FTC cases | | | |
| 2023-08-16 Can’t lose what you never had: Claims about digital ownership and creation in the age of generative AI | | | |
| 2023-08-22 For business opportunity sellers, FTC says “AI” stands for “allegedly inaccurate” | | | |
| 2023-09-15 Updated FTC-HHS publication outlines privacy and security laws and rules that impact consumer health data | | | |
| 2023-09-18 Companies warned about consequences of loose use of consumers’ confidential data | | | |
| 2023-09-27 Could PrivacyCon 2024 be the place to present your research on AI, privacy, or surveillance? | | | |
| 2022-05-20 Security Beyond Prevention: The Importance of Effective Breach Disclosures | | | |
| 2023-02-01 Security Principles: Addressing underlying causes of risk in complex systems | | | |
| 2023-06-29 Generative AI Raises Competition Concerns | | | |
| 2023-12-19 Coming face to face with Rite Aid’s allegedly unfair use of facial recognition technology | | | |
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| Children's Online Privacy Protection Rule ("COPPA") | | | |
| Privacy Policy | | | |
| Software as a Medical Device (SAMD) guidance (December 8, 2017) | | | |
| Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities, GAO-21-519SP | | | |
| Central Security Service, Artificial Intelligence Security Center | | | |
| Final Report | | | |
| 2021 Model Risk Management Handbook | | | |
| The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines | | | |
| Principles of Artificial Intelligence Ethics for the Intelligence Community | | | |
| SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence | | | |
| Public Views on Artificial Intelligence and Intellectual Property Policy | | | |
| Design principles | | | |
| California Consumer Privacy Act (CCPA) | | | |
| California Department of Justice, How to Read a Privacy Policy | | | |
| California Department of Technology, GenAI Executive Order | | | |
| California Privacy Rights Act (CPRA) | | | |
| Department of Technology, Office of Information Security, Generative Artificial Intelligence Risk Assessment, SIMM 5305-F, March 2024 | | | |
| Mississippi Department of Education, Artificial Intelligence Guidance for K-12 Classrooms | | | |
| New York City Automated Decision Systems Task Force Report (November 2019) | | | |
| RE: Use of External Consumer Data and Information Sources in Underwriting for Life Insurance | | | |
| Federal Reserve Bank of Dallas, Regulation B, Equal Credit Opportunity, Credit Scoring Interpretations: Withdrawl of Proposed Business Credit Amendments, June 3, 1982 | | | |
| Questions from the Commission on Protecting Privacy and Preventing Discrimination | | | |
| Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment - Shaping Europe’s digital future - European Commission | | | |
| Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance / Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) |
| 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 | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Official Policy, Frameworks, and Guidance |
| The Digital Services Act package (EU Digital Services Act and Digital Markets Act) | | | |
| Civil liability regime for artificial intelligence | | | |
| European Parliament, Addressing AI risks in the workplace: Workers and algorithms | | | |
| European Parliament, The impact of the General Data Protection Regulation (GDPR) on artificial intelligence | | | |
| European Commission, Hiroshima Process International Guiding Principles for Advanced AI system | | | |
| Proposal for a directive on adapting non-contractual civil liability rules to artificial intelligence: Complementary impact assessment | | | |
| Artificial intelligence act: Council and Parliament strike a deal on the first rules for AI in the world | | | |
| Data Protection Authority of Belgium General Secretariat, Artificial Intelligence Systems and the GDPR: A Data Protection Perspective | | | |
| European Data Protection Board (EDPB), AI Auditing documents | | | |
| European Data Protection Supervisor, First EDPS Orientations for EUIs using Generative AI | | | |
| European Labour Authority (ELA), Artificial Intelligence and Algorithms in Risk Assessment: Addressing Bias, Discrimination and other Legal and Ethical Issues | | | |
| OECD.AI, The Bias Assessment Metrics and Measures Repository | | | |
| OECD, AI, data governance and privacy: Synergies and areas of international co-operation | | | |
| OECD, Open, Useful and Re-usable data (OURdata) Index: 2019 - Policy Paper | | | |
| AI in Precision Persuasion. Unveiling Tactics and Risks on Social Media | | | |
| Narrative Detection and Topic Modelling in the Baltics | | | |
| UNESCO, Artificial Intelligence: examples of ethical dilemmas | | | |
| UNESCO, Consultation paper on AI regulation: emerging approaches across the world | | | |
| Office of the United Nations High Commissioner for Human Rights | | | |
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| Algorithmic Accountability Act of 2023 | | | |
| Arizona, House Bill 2685 | | | |
| Australia, Data Availability and Transparency Act 2022 | | | |
| Australia, Privacy Act 1988 | | | |
| California, Consumer Privacy Act of 2018, Civil Code - DIVISION 3. OBLIGATIONS [1427 - 3273.69] | | | |
| Colorado, SB24-205 Consumer Protections for Artificial Intelligence, Concerning consumer protections in interactions with artificial intelligence systems | | | |
| European Union, General Data Protection Regulation (GDPR) | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Law Texts and Drafts / European Union, General Data Protection Regulation (GDPR) |
| Article 22 EU GDPR "Automated individual decision-making, including profiling" | | | |
Awesome Machine Learning Interpretability / Community and Official Guidance Resources / Law Texts and Drafts |
| Executive Order 13960 (2020-12-03), Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government | | | |
| Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence | | | |
| Facial Recognition and Biometric Technology Moratorium Act of 2020 | | | |
| Federal Consumer Online Privacy Rights Act (COPRA) | | | |
| 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) | | | |
| Illinois, Biometric Information Privacy Act | | | |
| Justice in Policing Act | | | |
| National Conference of State Legislatures (NCSL) 2020 Consumer Data Privacy Legislation | | | |
| Nebraska, LB1203 - Regulate artificial intelligence in media and political advertisements under the Nebraska Political Accountability and Disclosure Act | | | |
| Rhode Island, Executive Order 24-06: Artificial Intelligence and Data Centers of Excellence | | | |
| Virginia, Consumer Data Protection Act | | | |
| Washington State, SB 6513 - 2019-20 | | | |
| United States Congress, 118th Congress (2023-2024), H.R.5586 - DEEPFAKES Accountability Act | | | |
| United States Congress, 118th Congress (2023-2024), H.R. 9720, AI Incident Reporting and Security Enhancement Act | | | |
| United States Congress, 118th Congress (2023-2024), S.4769 - VET Artificial Intelligence Act | | | |
Awesome Machine Learning Interpretability / Education Resources / Comprehensive Software Examples and Tutorials |
| COMPAS Analysis Using Aequitas | 674 | 24 days ago | |
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| Getting a Window into your Black Box Model | | | |
| H20.ai, From GLM to GBM Part 1 | | | |
| H20.ai, From GLM to GBM Part 2 | | | |
| IML | | | |
| Interpretable Machine Learning with Python | 673 | 4 months ago | |
| Interpreting Machine Learning Models with the iml Package | | | |
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| Machine Learning Explainability by Kaggle Learn | | | |
| Model Interpretability with DALEX | | | |
Awesome Machine Learning Interpretability / Education Resources / Comprehensive Software Examples and Tutorials / Model Interpretability with DALEX / : |
| The Importance of Human Interpretable Machine Learning | | | |
| Model Interpretation Strategies | | | |
| Hands-on Machine Learning Model Interpretation | | | |
| Interpreting Deep Learning Models for Computer Vision | | | |
Awesome Machine Learning Interpretability / Education Resources / Comprehensive Software Examples and Tutorials |
| Partial Dependence Plots in R | | | |
Awesome Machine Learning Interpretability / Education Resources / Comprehensive Software Examples and Tutorials / PiML: |
| PiML Medium Tutorials | | | |
| PiML-Toolbox Examples | 927 | 3 months ago | |
Awesome Machine Learning Interpretability / Education Resources / Comprehensive Software Examples and Tutorials |
| Reliable-and-Trustworthy-AI-Notebooks | 1 | almost 3 years ago | |
| Saliency Maps for Deep Learning | | | |
| Visualizing ML Models with LIME | | | |
| Visualizing and debugging deep convolutional networks | | | |
| What does a CNN see? | | | |
Awesome Machine Learning Interpretability / Education Resources / Free-ish Books |
| César A. Hidalgo, Diana Orghian, Jordi Albo-Canals, Filipa de Almeida, and Natalia Martin, 2021, How Humans Judge Machines | | | |
| Charles Perrow, 1984, Normal Accidents: Living with High-Risk Technologies | | | |
| Charles Perrow, 1999, Normal Accidents: Living with High-Risk Technologies with a New Afterword and a Postscript on the Y2K Problem | | | |
| Christoph Molnar, 2021, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable | | | |
Awesome Machine Learning Interpretability / Education Resources / Free-ish Books / Christoph Molnar, 2021, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable |
| christophM/interpretable-ml-book | 4,756 | 2 months ago | |
Awesome Machine Learning Interpretability / Education Resources / Free-ish Books |
| Deborah G. Johnson and Keith W. Miller, 2009, Computer Ethics: Analyzing Information Technology, Fourth Edition | | | |
| Ed Dreby and Keith Helmuth (contributors) and Judy Lumb (editor), 2009, Fueling Our Future: A Dialogue about Technology, Ethics, Public Policy, and Remedial Action | | | |
| Ethics for people who work in tech | | | |
| Florence G'sell, Regulating under Uncertainty: Governance Options for Generative AI | | | |
| George Reynolds, 2002, Ethics in Information Technology | | | |
| George Reynolds, 2002, Ethics in Information Technology, Instructor's Edition | | | |
| Joseph Weizenbaum, 1976, Computer Power and Human Reason: From Judgment to Calculation | | | |
| Kenneth Vaux (editor), 1970, Who Shall Live? Medicine, Technology, Ethics | | | |
| Kush R. Varshney, 2022, Trustworthy Machine Learning: Concepts for Developing Accurate, Fair, Robust, Explainable, Transparent, Inclusive, Empowering, and Beneficial Machine Learning Systems | | | |
| Marsha Cook Woodbury, 2003, Computer and Information Ethics | | | |
| M. David Ermann, Mary B. Williams, and Claudio Gutierrez, 1990, Computers, Ethics, and Society | | | |
| Morton E. Winston and Ralph D. Edelbach, 2000, Society, Ethics, and Technology, First Edition | | | |
| Morton E. Winston and Ralph D. Edelbach, 2003, Society, Ethics, and Technology, Second Edition | | | |
| Morton E. Winston and Ralph D. Edelbach, 2006, Society, Ethics, and Technology, Third Edition | | | |
| Patrick Hall and Navdeep Gill, 2019, An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI, Second Edition | | | |
| Patrick Hall, Navdeep Gill, and Benjamin Cox, 2021, Responsible Machine Learning: Actionable Strategies for Mitigating Risks & Driving Adoption | | | |
| Paula Boddington, 2017, Towards a Code of Ethics for Artificial Intelligence | | | |
| Przemyslaw Biecek and Tomasz Burzykowski, 2020, Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python | | | |
| Przemyslaw Biecek, 2023, Adversarial Model Analysis | | | |
| Raymond E. Spier (editor), 2003, Science and Technology Ethics | | | |
| Richard A. Spinello, 1995, Ethical Aspects of Information Technology | | | |
| Richard A. Spinello, 1997, Case Studies in Information and Computer Ethics | | | |
| Richard A. Spinello, 2003, Case Studies in Information Technology Ethics, Second Edition | | | |
| Solon Barocas, Moritz Hardt, and Arvind Narayanan, 2022, Fairness and Machine Learning: Limitations and Opportunities | | | |
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| Stephen H. Unger, 1982, Controlling Technology: Ethics and the Responsible Engineer, First Edition | | | |
| Stephen H. Unger, 1994, Controlling Technology: Ethics and the Responsible Engineer, Second Edition | | | |
Awesome Machine Learning Interpretability / Education Resources / Glossaries and Dictionaries |
| A.I. For Anyone: The A-Z of AI | | | |
| The Alan Turing Institute: Data science and AI glossary | | | |
| Appen Artificial Intelligence Glossary | | | |
| Artificial intelligence and illusions of understanding in scientific research (glossary on second page) | | | |
| Brookings: The Brookings glossary of AI and emerging technologies | | | |
| Built In, Responsible AI Explained | | | |
| Center for Security and Emerging Technology: Glossary | | | |
| CompTIA: Artificial Intelligence (AI) Terminology: A Glossary for Beginners | | | |
| Council of Europe Artificial Intelligence Glossary | | | |
| Coursera: Artificial Intelligence (AI) Terms: A to Z Glossary | | | |
| Dataconomy: AI dictionary: Be a native speaker of Artificial Intelligence | | | |
| Dennis Mercadal, 1990, Dictionary of Artificial Intelligence | | | |
| European Commission, EU-U.S. Terminology and Taxonomy for Artificial Intelligence - Second Edition | | | |
| European Commission, Glossary of human-centric artificial intelligence | | | |
| G2: 70+ A to Z Artificial Intelligence Terms in Technology | | | |
| General Services Administration: AI Guide for Government: Key AI terminology | | | |
| Google Developers Machine Learning Glossary | | | |
| H2O.ai Glossary | | | |
| IAPP Glossary of Privacy Terms | | | |
| IAPP International Definitions of Artificial Intelligence | | | |
| IAPP Key Terms for AI Governance | | | |
| IBM AI glossary | | | |
| IEEE, A Glossary for Discussion of Ethics of Autonomous and Intelligent Systems, Version 1 | | | |
| ISO/IEC DIS 22989(en) Information technology — Artificial intelligence — Artificial intelligence concepts and terminology | | | |
| Jerry M. Rosenberg, 1986, Dictionary of Artificial Intelligence & Robotics | | | |
| MakeUseOf: A Glossary of AI Jargon: 29 AI Terms You Should Know | | | |
| Moveworks: AI Terms Glossary | | | |
| National Institute of Standards and Technology (NIST), NIST AI 100-2 E2023: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations | | | |
| National Institute of Standards and Technology (NIST), The Language of Trustworthy AI: An In-Depth Glossary of Terms | | | |
| Oliver Houdé, 2004, Dictionary of Cognitive Science: Neuroscience, Psychology, Artificial Intelligence, Linguistics, and Philosophy | | | |
| Open Access Vocabulary | | | |
| Otto Vollnhals, 1992, A Multilingual Dictionary of Artificial Intelligence (English, German, French, Spanish, Italian) | | | |
| Raoul Smith, 1989, The Facts on File Dictionary of Artificial Intelligence | | | |
| Raoul Smith, 1990, Collins Dictionary of Artificial Intelligence | | | |
| Salesforce: AI From A to Z: The Generative AI Glossary for Business Leaders | | | |
| Siemens, Artificial Intelligence Glossary | | | |
| Stanford University HAI Artificial Intelligence Definitions | | | |
| TechTarget: Artificial intelligence glossary: 60+ terms to know | | | |
| TELUS International: 50 AI terms every beginner should know | | | |
| Towards AI, Generative AI Terminology — An Evolving Taxonomy To Get You Started | | | |
| UK Parliament, Artificial intelligence (AI) glossary | | | |
| University of New South Wales, Bill Wilson, The Machine Learning Dictionary | | | |
| VAIR (Vocabulary of AI Risks) | | | |
| Wikipedia: Glossary of artificial intelligence | | | |
| William J. Raynor, Jr, 1999, The International Dictionary of Artificial Intelligence, First Edition | | | |
| William J. Raynor, Jr, 2009, International Dictionary of Artificial Intelligence, Second Edition | | | |
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| An Introduction to Data Ethics | | | |
| Awesome LLM Courses | 86 | 4 days ago | |
| AWS Skill Builder | | | |
| Build a Large Language Model (From Scratch) | 27,618 | 11 days ago | |
| Carnegie Mellon University, Computational Ethics for NLP | | | |
| Certified Ethical Emerging Technologist | | | |
| Coursera, DeepLearning.AI, Generative AI for Everyone | | | |
| Coursera, DeepLearning.AI, Generative AI with Large Language Models | | | |
| Coursera, Google Cloud, Introduction to Generative AI | | | |
| Coursera, Vanderbilt University, Prompt Engineering for ChatGPT | | | |
| CS103F: Ethical Foundations of Computer Science | | | |
| DeepLearning.AI | | | |
| ETH Zürich ReliableAI 2022 Course Project repository | 1 | over 1 year ago | |
| Fairness in Machine Learning | | | |
| Fast.ai Data Ethics course | | | |
| Google Cloud Skills Boost | | | |
Awesome Machine Learning Interpretability / Education Resources / Open-ish Classes / Google Cloud Skills Boost |
| Attention Mechanism | | | |
| Create Image Captioning Models | | | |
| Encoder-Decoder Architecture | | | |
| Introduction to Generative AI | | | |
| Introduction to Image Generation | | | |
| Introduction to Large Language Models | | | |
| Introduction to Responsible AI | | | |
| Introduction to Vertex AI Studio | | | |
| Transformer Models and BERT Model | | | |
Awesome Machine Learning Interpretability / Education Resources / Open-ish Classes |
| Grow with Google, Generative AI for Educators | | | |
| Human-Centered Machine Learning | | | |
| IBM SkillsBuild | | | |
| Introduction to AI Ethics | | | |
| INFO 4270: Ethics and Policy in Data Science | | | |
| Introduction to Responsible Machine Learning | | | |
| Jay Alammar, Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) | | | |
| Machine Learning Fairness by Google | | | |
| OECD.AI, Disability-Centered AI And Ethics MOOC | | | |
| Piotr Sapieżyński's CS 4910 - Special Topics in Computer Science: Algorithm Audits | | | |
| Tech & Ethics Curricula | | | |
| Trustworthy Deep Learning | | | |
Awesome Machine Learning Interpretability / Education Resources / Podcasts and Channels |
| Internet of Bugs | | | |
| Tech Won't Save Us | | | |
| This Is Technology Ethics: An Introduction | | | |
| |
| AI Incident Database (Responsible AI Collaborative) | | | |
| AI Vulnerability Database (AVID) | | | |
| AIAAIC | | | |
| AI Badness: An open catalog of generative AI badness | | | |
| AI Risk Database | | | |
| Brennan Center for Justice, Artificial Intelligence Legislation Tracker | | | |
| EthicalTech@GW, Deepfakes & Democracy Initiative | | | |
| George Washington University Law School's AI Litigation Database | | | |
| Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database | | | |
| OECD AI Incidents Monitor | | | |
| Verica Open Incident Database (VOID) | | | |
| The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone Mobile | | | |
| Omission and Commission Errors Underlying AI Failures | | | |
| When Your AI Becomes a Target: AI Security Incidents and Best Practices | | | |
| Planning for Natural Language Failures with the AI Playbook | | | |
| SoK: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security | | | |
| Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database | | | |
| Indexing AI Risks with Incidents, Issues, and Variants | | | |
| Artificial Intelligence Incidents & Ethics: A Narrative Review | | | |
| Deployment Corrections: An Incident Response Framework for Frontier AI Models | | | |
| Ontologies for Reasoning about Failures in AI Systems | | | |
| Good Systems, Bad Data?: Interpretations of AI Hype and Failures | | | |
| Exploring Trust With the AI Incident Database | | | |
| AI Ethics Issues in Real World: Evidence from AI Incident Database | | | |
| Understanding and Avoiding AI Failures: A Practical Guide | | | |
| Artificial Intelligence Safety and Cybersecurity: A Timeline of AI Failures | | | |
Awesome Machine Learning Interpretability / AI Incidents, Critiques, and Research Resources / AI Law, Policy, and Guidance Trackers |
| Access Now, Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report | | | |
| The Ethical AI Database | | | |
| George Washington University Law School's AI Litigation Database | | | |
| IAPP Global AI Legislation Tracker | | | |
| IAPP US State Privacy Legislation Tracker | | | |
| Institute for the Future of Work, Tracking international legislation relevant to AI at work | | | |
| Legal Nodes, Global AI Regulations Tracker: Europe, Americas & Asia-Pacific Overview | | | |
| National Conference of State Legislatures, Deceptive Audio or Visual Media (‘Deepfakes’) 2024 Legislation | | | |
| OECD.AI, National AI policies & strategies | | | |
| Raymond Sun, Global AI Regulation Tracker | | | |
| Runway Strategies, Global AI Regulation Tracker | | | |
| University of North Texas, Artificial Intelligence (AI) Policy Collection | | | |
| VidhiSharma.AI, Global AI Governance Tracker | | | |
| White & Case, AI Watch: Global regulatory tracker - United States | | | |
Awesome Machine Learning Interpretability / AI Incidents, Critiques, and Research Resources / Challenges and Competitions |
| FICO Explainable Machine Learning Challenge | | | |
| OSD Bias Bounty | | | |
| National Fair Housing Alliance Hackathon | | | |
| Twitter Algorithmic Bias | | | |
Awesome Machine Learning Interpretability / AI Incidents, Critiques, and Research Resources / Critiques of AI |
| Against predictive optimization | | | |
| AI can only do 5% of jobs, says MIT economist who fears tech stock crash | | | |
| AI chatbots use racist stereotypes even after anti-racism training | | | |
| AI coding assistants do not boost productivity or prevent burnout, study finds | | | |
| AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business | | | |
| AI hype, promotional culture, and affective capitalism | | | |
| AI Is a Lot of Work | | | |
| 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 | | | |
| AI Safety Is a Narrative Problem | | | |
| AI Snake Oil | | | |
| AI Tools Still Permitting Political Disinfo Creation, NGO Warns | | | |
| Anthropomorphism in AI: hype and fallacy | | | |
| Are Emergent Abilities of Large Language Models a Mirage? | | | |
| Are Language Models Actually Useful for Time Series Forecasting? | | | |
| Artificial Hallucinations in ChatGPT: Implications in Scientific Writing | | | |
| Artificial intelligence and illusions of understanding in scientific research | | | |
| Artificial Intelligence: Hope for Future or Hype by Intellectuals? | | | |
| ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs | | | |
| Aylin Caliskan's publications | | | |
| Beyond Preferences in AI Alignment | | | |
| Chatbots in consumer finance | | | |
| ChatGPT is bullshit | | | |
| Companies like Google and OpenAI are pillaging the internet and pretending it’s progress | | | |
| Consciousness in Artificial Intelligence: Insights from the Science of Consciousness | | | |
| The Cult of AI | | | |
| Data and its (dis)contents: A survey of dataset development and use in machine learning research | | | |
| The Data Scientific Method vs. The Scientific Method | | | |
| Ed Zitron's Where's Your Ed At | | | |
| Emergent and Predictable Memorization in Large Language Models | | | |
| Evaluating Language-Model Agents on Realistic Autonomous Tasks | | | |
| FABLES: Evaluating faithfulness and content selection in book-length summarization | | | |
| The Fallacy of AI Functionality | | | |
| Futurism, Disillusioned Businesses Discovering That AI Kind of Sucks | | | |
| Gen AI: Too Much Spend, Too Little Benefit? | | | |
| Generative AI: UNESCO study reveals alarming evidence of regressive gender stereotypes | | | |
| Get Ready for the Great AI Disappointment | | | |
| Ghost in the Cloud: Transhumanism’s simulation theology | | | |
| Handling the hype: Implications of AI hype for public interest tech projects | | | |
| The harms of terminology: why we should reject so-called “frontier AI” | | | |
| HealthManagement.org, The Journal, Volume 19, Issue 2, 2019, Artificial Hype | | | |
| How AI hype impacts the LGBTQ + community | | | |
| How AI lies, cheats, and grovels to succeed - and what we need to do about it | | | |
| Identifying and Eliminating CSAM in Generative ML Training Data and Models | | | |
| Insanely Complicated, Hopelessly Inadequate | | | |
| Internet of Bugs, Debunking Devin: "First AI Software Engineer" Upwork lie exposed! (video) | | | |
| I Will Fucking Piledrive You If You Mention AI Again | | | |
| Julia Angwin, Press Pause on the Silicon Valley Hype Machine | | | |
| Lazy use of AI leads to Amazon products called “I cannot fulfill that request” | | | |
| Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs | | | |
| LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks | | | |
| Long-context LLMs Struggle with Long In-context Learning | | | |
| Low-Resource Languages Jailbreak GPT-4 | | | |
| Machine Learning: The High Interest Credit Card of Technical Debt | | | |
| Measuring the predictability of life outcomes with a scientific mass collaboration | | | |
| Meta AI Chief: Large Language Models Won't Achieve AGI | | | |
| Meta’s AI chief: LLMs will never reach human-level intelligence | | | |
| Most CEOs aren’t buying the hype on generative AI benefits | | | |
| Nepotistically Trained Generative-AI Models Collapse | | | |
| Non-discrimination Criteria for Generative Language Models | | | |
| Open Problems in Technical AI Governance | | | |
| On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? | | | |
| The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’ | | | |
| Pivot to AI | | | |
| Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models | | | |
| The Price of Emotion: Privacy, Manipulation, and Bias in Emotional AI | | | |
| Promising the future, encoding the past: AI hype and public media imagery | | | |
| Quantifying Memorization Across Neural Language Models | | | |
| Re-evaluating GPT-4’s bar exam performance | | | |
| Researchers surprised by gender stereotypes in ChatGPT | | | |
| Ryan Allen, Explainable AI: The What’s and Why’s, Part 1: The What | | | |
| Scalable Extraction of Training Data from (Production) Language Models | | | |
| Speed of AI development stretches risk assessments to breaking point | | | |
| Talking existential risk into being: a Habermasian critical discourse perspective to AI hype | | | |
| Task Contamination: Language Models May Not Be Few-Shot Anymore | | | |
| Theory Is All You Need: AI, Human Cognition, and Decision Making | | | |
| There Is No A.I. | | | |
| Three different types of AI hype in healthcare | | | |
| Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context | | | |
| We still don't know what generative AI is good for | | | |
| What’s in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT | | | |
| Which Humans? | | | |
| Why We Must Resist AI’s Soft Mind Control | | | |
| Winner's Curse? On Pace, Progress, and Empirical Rigor | | | |
| A bottle of water per email: the hidden environmental costs of using AI chatbots | | | |
| AI already uses as much energy as a small country. It’s only the beginning. | | | |
| The AI Carbon Footprint and Responsibilities of AI Scientists | | | |
| Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models | | | |
| The carbon impact of artificial intelligence | | | |
| Data centre water consumption | | | |
| Environment and sustainability development: A ChatGPT perspective | | | |
| The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT | | | |
| The Environmental Price of Intelligence: Evaluating the Social Cost of Carbon in Machine Learning | | | |
| Generative AI’s environmental costs are soaring — and mostly secret | | | |
| The Hidden Environmental Impact of AI | | | |
| Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models | | | |
| Microsoft’s Hypocrisy on AI | | | |
| The mechanisms of AI hype and its planetary and social costs | | | |
| Power Hungry Processing: Watts Driving the Cost of AI Deployment? | | | |
| Promoting Sustainability: Mitigating the Water Footprint in AI-Embedded Data Centres | | | |
| Sustainable AI: Environmental Implications, Challenges and Opportunities | | | |
Awesome Machine Learning Interpretability / AI Incidents, Critiques, and Research Resources / Groups and Organizations |
| AI Forum New Zealand, AI Governance Working Group | | | |
| AI Village | | | |
| Center for Advancing Safety of Machine Intelligence | | | |
| Center for AI and Digital Policy | | | |
| Center for Democracy and Technology | | | |
| Center for Security and Emerging Technology | | | |
| Future of Life Institute | | | |
| Institute for Advanced Study (IAS), AI Policy and Governance Working Group | | | |
| Partnership on AI | | | |
Awesome Machine Learning Interpretability / AI Incidents, Critiques, and Research Resources / Curated Bibliographies |
| Proposed Guidelines for Responsible Use of Explainable Machine Learning (presentation, bibliography) | 20 | over 4 years ago | |
| Proposed Guidelines for Responsible Use of Explainable Machine Learning (paper, bibliography) | 17 | over 1 year ago | |
| A Responsible Machine Learning Workflow (paper, bibliography) | 13 | over 4 years ago | |
| Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship | | | |
Awesome Machine Learning Interpretability / AI Incidents, Critiques, and Research Resources / List of Lists |
| A Living and Curated Collection of Explainable AI Methods | | | |
| AI Ethics Guidelines Global Inventory | | | |
| AI Ethics Resources | | | |
| AI Tools and Platforms | | | |
| Awesome AI Guidelines | 1,246 | 16 days ago | |
| Awesome interpretable machine learning | 902 | over 1 year ago | |
| Awesome-explainable-AI | 1,398 | 4 days ago | |
| Awesome-ML-Model-Governance | 98 | 6 months ago | |
| Awesome MLOps | 12,525 | 4 months ago | |
| Awesome Production Machine Learning | 17,404 | 4 days ago | |
| Awful AI | 6,951 | 5 months ago | |
| Casey Fiesler's AI Ethics & Policy News spreadsheet | | | |
| criticalML | 367 | over 5 years ago | |
| Ethics for people who work in tech | | | |
| Evaluation Repository for 'Sociotechnical Safety Evaluation of Generative AI Systems' | | | |
| IMDA-BTG, LLM-Evals-Catalogue | 11 | 11 months ago | |
| Machine Learning Ethics References | 59 | over 1 year ago | |
| Machine Learning Interpretability Resources | 481 | almost 4 years ago | |
| NIST, Comments Received for RFI on Artificial Intelligence Risk Management Framework | | | |
| OECD-NIST Catalogue of AI Tools and Metrics | | | |
| OpenAI Cookbook | 58,697 | 11 days ago | |
| private-ai-resources | 469 | over 4 years ago | |
| Ravit Dotan's Resources | | | |
| ResponsibleAI | | | |
| Tech & Ethics Curricula | | | |
| Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance | | | |
| XAI Resources | 816 | over 2 years ago | |
| xaience | 107 | about 1 year ago | |
Awesome Machine Learning Interpretability / Technical Resources / Benchmarks |
| benchm-ml | 1,870 | about 2 years ago | |
| Bias Benchmark for QA dataset (BBQ) | 83 | 9 months ago | |
| Cataloguing LLM Evaluations | 11 | 11 months ago | |
| DecodingTrust | 251 | 19 days ago | |
| EleutherAI, Language Model Evaluation Harness | 6,596 | 1 day ago | |
| GEM | | | |
| HELM | | | |
| Hugging Face, evaluate | 1,976 | 19 days ago | |
| i-gallegos, Fair-LLM-Benchmark | 105 | about 1 year ago | |
| MLCommons, MLCommons AI Safety v0.5 Proof of Concept | | | |
| MLCommons, Introducing v0.5 of the AI Safety Benchmark from MLCommons | | | |
| Nvidia MLPerf | | | |
| OpenML Benchmarking Suites | | | |
| Real Toxicity Prompts (Allen Institute for AI) | | | |
| SafetyPrompts.com | | | |
| Sociotechnical Safety Evaluation Repository | | | |
| TrustLLM-Benchmark | | | |
| Trust-LLM-Benchmark Leaderboard | | | |
| TruthfulQA | 588 | 11 months ago | |
| WAVES: Benchmarking the Robustness of Image Watermarks | | | |
| Wild-Time: A Benchmark of in-the-Wild Distribution Shifts over Time | 61 | over 1 year ago | |
| Winogender Schemas | 65 | over 5 years ago | |
| yandex-research / tabred | 36 | 2 months ago | |
Awesome Machine Learning Interpretability / Technical Resources / Common or Useful Datasets |
| Adult income dataset | | | |
| Balanced Faces in the Wild | 45 | almost 2 years ago | |
| Bruegel, A dataset on EU legislation for the digital world | | | |
| COMPAS Recidivism Risk Score Data and Analysis | | | |
Awesome Machine Learning Interpretability / Technical Resources / Common or Useful Datasets / COMPAS Recidivism Risk Score Data and Analysis / : |
| All Lending Club loan data | | | |
| Amazon Open Data | | | |
| Data.gov | | | |
| Home Mortgage Disclosure Act (HMDA) Data | | | |
| MIMIC-III Clinical Database | | | |
| UCI ML Data Repository | | | |
Awesome Machine Learning Interpretability / Technical Resources / Common or Useful Datasets |
| FANNIE MAE Single Family Loan Performance | | | |
| Have I Been Trained? | | | |
| nikhgarg / EmbeddingDynamicStereotypes | 158 | over 1 year ago | |
| Presidential Deepfakes Dataset | | | |
| NYPD Stop, Question and Frisk Data | | | |
| socialfoundations / folktables | 236 | 5 months ago | |
| Statlog (German Credit Data) | | | |
| Wikipedia Talk Labels: Personal Attacks | | | |
| |
| CometLLM | 1,480 | 4 days ago | |
| dvc | | | |
| gigantum | | | |
| mlflow | | | |
| mlmd | 616 | about 1 month ago | |
| modeldb | 1,696 | 2 months ago | |
| neptune | | | |
| |
| LLM Dataset Inference: Did you train on my dataset? | 21 | 2 months ago | |
Awesome Machine Learning Interpretability / Technical Resources / Open Source/Access Responsible AI Software Packages |
| DiscriLens | 6 | over 1 year ago | |
| Hugging Face, BiasAware: Dataset Bias Detection | | | |
| manifold | 1,645 | over 1 year ago | |
| PAIR-code / datacardsplaybook | 171 | 4 months ago | |
| PAIR-code / facets | 7,348 | over 1 year ago | |
| PAIR-code / knowyourdata | 283 | almost 2 years ago | |
| TensorBoard Projector | | | |
| What-if Tool | | | |
| Born-again Tree Ensembles | 63 | about 1 year ago | |
| Certifiably Optimal RulE ListS | 170 | almost 3 years ago | |
| Secure-ML | 36 | about 5 years ago | |
| LDNOOBW | | | |
| acd | 124 | about 3 years ago | |
| aequitas | 674 | 24 days ago | |
| AI Fairness 360 | 2,411 | 3 months ago | |
| AI Explainability 360 | 1,612 | 3 months ago | |
| ALEPython | 155 | over 1 year ago | |
| Aletheia | 70 | 9 months ago | |
| allennlp | 11,741 | almost 2 years ago | |
| algofairness | | | |
| http://fairness.haverford.edu/ | | | See [Algorithmic Fairness][ ) |
| Alibi | 2,391 | 3 months ago | |
| anchor | 794 | about 2 years ago | |
| Bayesian Case Model | | | |
| Bayesian Ors-Of-Ands | 34 | over 2 years ago | |
| Bayesian Rule List (BRL) | | | |
| BlackBoxAuditing | 130 | over 1 year ago | |
| CalculatedContent, WeightWatcher | 1,446 | 24 days ago | |
| casme | 73 | over 4 years ago | |
| Causal Discovery Toolbox | 1,113 | 6 months ago | |
| captum | 4,830 | 11 days ago | |
| causalml | 5,005 | 4 days ago | |
| cdt15, Causal Discovery Lab., Shiga University | | | |
| checklist | 2,001 | 9 months ago | |
| cleverhans | 6,167 | 6 months ago | |
| contextual-AI | 85 | about 1 year ago | |
| ContrastiveExplanation (Foil Trees) | 45 | over 1 year ago | |
| counterfit | 801 | about 1 year ago | |
| dalex | 1,365 | about 1 month ago | |
| debiaswe | 243 | over 1 year ago | |
| DeepExplain | 729 | about 4 years ago | |
| DeepLIFT | 819 | over 2 years ago | |
| deepvis | 4,010 | over 4 years ago | |
| DIANNA | 45 | 8 days ago | |
| DiCE | 1,341 | 6 months ago | |
| DoWhy | 7,031 | about 1 month ago | |
| dtreeviz | 2,939 | about 1 month ago | |
| ecco | 1,970 | about 2 months ago | |
| eli5 | 2,753 | over 2 years ago | |
| explabox | 14 | 7 months ago | |
| Explainable Boosting Machine (EBM)/GA2M | 6,237 | 5 days ago | |
| ExplainaBoard | 360 | over 1 year ago | |
| explainerdashboard | 2,295 | 3 months ago | |
| explainX | 407 | about 1 month ago | |
| fair-classification | 188 | over 2 years ago | |
| fairml | 359 | over 3 years ago | |
| fairlearn | 1,909 | 11 days ago | |
| fairness-comparison | 159 | almost 2 years ago | |
| fairness_measures_code | 38 | 7 months ago | |
| Falling Rule List (FRL) | | | |
| foolbox | 2,731 | 6 months ago | |
| Giskard | 3,920 | 10 days ago | |
| Grad-CAM | | | (GitHub topic) |
| gplearn | 1,593 | 10 months ago | |
| H2O-3 | 6,873 | 6 days ago | |
| H2O-3 | 6,873 | 6 days ago | |
| H2O-3 | 6,873 | 6 days ago | |
| h2o-LLM-eval | 49 | about 1 year ago | |
| hate-functional-tests | 55 | almost 3 years ago | |
| imodels | 1,373 | 14 days ago | |
| iNNvestigate neural nets | 1,256 | 10 months ago | |
| Integrated-Gradients | 595 | over 2 years ago | |
| interpret | 6,237 | 5 days ago | |
| interpret_with_rules | 21 | 2 months ago | |
| InterpretME | 24 | 7 months ago | |
| Keras-vis | 2,978 | over 2 years ago | |
| keract | 1,041 | about 2 months ago | |
| L2X | 123 | over 3 years ago | |
| Learning to Explain: An Information-Theoretic Perspective on Model Interpretation | | | "Code for replicating the experiments in the paper at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan.” |
| langtest | 494 | 11 days ago | |
| learning-fair-representations | 26 | about 4 years ago | |
| http://www.cs.toronto.edu/~toni/Papers/icml-final.pdf | | | "Python numba implementation of Zemel et al. 2013 " |
| leeky: Leakage/contamination testing for black box language models | 6 | 7 months ago | |
| leondz / garak, LLM vulnerability scanner | 1,294 | 11 days ago | |
| lilac | 937 | 7 months ago | |
| lime | 11,528 | 2 months ago | |
| LiFT | 168 | over 1 year ago | |
| lit | 3,468 | 10 days ago | |
| LLM Dataset Inference: Did you train on my dataset? | 21 | 2 months ago | |
| lofo-importance | 816 | 9 months ago | |
| lrp_toolbox | 327 | over 2 years ago | |
| MindsDB | 26,362 | 3 days ago | |
| MLextend | | | |
| ml-fairness-gym | 311 | over 1 year ago | |
| ml_privacy_meter | 587 | about 1 month ago | |
| mllp | 21 | 7 months ago | |
| Transparent Classification with Multilayer Logical Perceptrons and Random Binarization | | | "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: .” |
| Monotonic Constraints | | | |
| XGBoost | | | |
| Multilayer Logical Perceptron (MLLP) | 21 | 7 months ago | |
| Transparent Classification with Multilayer Logical Perceptrons and Random Binarization | | | "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: .” |
| OptBinning | 451 | 4 months ago | |
| Optimal Sparse Decision Trees | 98 | over 1 year ago | |
| "Optimal Sparse Decision Trees" | | | "This accompanies the paper, by Xiyang Hu, Cynthia Rudin, and Margo Seltzer.” |
| parity-fairness | | | |
| PDPbox | 841 | about 1 month ago | |
| PiML-Toolbox | 927 | 3 months ago | |
| pjsaelin / Cubist | 42 | about 1 month ago | |
| Privacy-Preserving-ML | 1 | over 1 year ago | |
| ProtoPNet | | | |
| pyBreakDown | 41 | about 1 year ago | |
| dalex | | | See |
| PyCEbox | 164 | over 4 years ago | |
| pyGAM | 869 | 4 months ago | |
| pymc3 | 8,664 | 3 days ago | |
| pySS3 | 333 | about 1 year ago | |
| pytorch-grad-cam | 10,272 | about 1 month ago | |
| pytorch-innvestigate | 9 | over 5 years ago | |
| https://github.com/albermax/innvestigate/ | 1,256 | 10 months ago | "PyTorch implementation of Keras already existing project: .” |
| Quantus | 531 | 15 days ago | |
| rationale | 354 | about 6 years ago | |
| responsibly | 92 | 11 months ago | |
| REVISE: REvealing VIsual biaSEs | 111 | about 2 years ago | |
| robustness | 910 | 9 months ago | |
| MadryLab | | | "a package we (students in the ) created to make training, evaluating, and exploring neural networks flexible and easy.” |
| RISE | 150 | over 4 years ago | |
| Vitali Petsiuk | | | "contains source code necessary to reproduce some of the main results in the paper: , , (BMVC, 2018) [and] .” |
| Risk-SLIM | 130 | over 1 year ago | |
| SAGE | 246 | 12 days ago | |
| SALib | 872 | 15 days ago | |
| Scikit-Explain | | | |
| Scikit-learn | | | |
| Scikit-learn | | | |
| Scikit-learn | | | |
| scikit-fairness | 29 | over 3 years ago | |
| fairlearn | | | Historical link. Merged with |
| scikit-multiflow | | | |
| shap | 22,601 | 4 days ago | |
| shapley | 218 | over 1 year ago | |
| sklearn-expertsys | 488 | about 7 years ago | |
| skope-rules | 613 | 8 months ago | |
| solas-ai-disparity | 34 | 6 months ago | |
| Super-sparse Linear Integer models (SLIMs) | 41 | 12 months ago | |
| tensorflow/lattice | 518 | 2 months ago | |
| tensorflow/lucid | 4,653 | over 1 year ago | |
| tensorflow/fairness-indicators | 341 | about 1 month ago | |
| tensorflow/model-analysis | 1,256 | 4 days ago | |
| tensorflow/model-card-toolkit | 419 | about 1 year ago | |
| tensorflow/model-remediation | 43 | over 1 year ago | |
| tensorflow/privacy | 1,925 | 15 days ago | |
| tensorflow/tcav | 628 | 2 months ago | |
| tensorfuzz | 208 | about 6 years ago | |
| TensorWatch | 3,412 | about 1 year ago | |
| TextFooler | 486 | almost 2 years ago | |
| text_explainability | | | |
| text_sensitivity | | | |
| tf-explain | 1,016 | 4 months ago | |
| Themis | 101 | about 4 years ago | |
| themis-ml | 124 | almost 4 years ago | |
| TorchUncertainty | 281 | 9 days ago | |
| treeinterpreter | 744 | about 1 year ago | |
| TRIAGE | 7 | 7 months ago | |
| woe | 255 | almost 5 years ago | |
| xai | 1,103 | almost 3 years ago | |
| xdeep | 42 | over 4 years ago | |
| xplique | 624 | 10 days ago | |
| ydata-profiling | 12,415 | 9 days ago | |
| yellowbrick | 4,270 | about 1 year ago | |
| ALEPlot | | | |
| arules | | | |
| Causal SVM | 5 | over 6 years ago | |
| DALEX | 1,365 | about 1 month ago | |
| DALEXtra: Extension for 'DALEX' Package | | | |
| DrWhyAI | 679 | over 1 year ago | |
| elasticnet | | | |
| ExplainPrediction | 2 | over 7 years ago | |
| Explainable Boosting Machine (EBM)/GA2M | | | |
| fairmodels | 86 | about 2 years ago | |
| fairness | | | |
| fastshap | 114 | 8 months ago | |
| featureImportance | 33 | over 3 years ago | |
| flashlight | 22 | 2 months ago | |
| forestmodel | | | |
| fscaret | | | |
| gam | | | |
| glm2 | | | |
| glmnet | | | |
| H2O-3 | 6,873 | 6 days ago | |
| H2O-3 | 6,873 | 6 days ago | |
| H2O-3 | 6,873 | 6 days ago | |
| iBreakDown | 79 | 10 months ago | |
| ICEbox: Individual Conditional Expectation Plot Toolbox | | | |
| iml | 491 | 3 days ago | |
| ingredients | 37 | over 1 year ago | |
| interpret: Fit Interpretable Machine Learning Models | | | |
| lightgbmExplainer | 24 | about 5 years ago | |
| lime | 481 | about 2 years ago | |
| live | | | |
| mcr | 8 | almost 5 years ago | |
| modelDown | | | |
| modelOriented | | | |
| modelStudio | 323 | about 1 year ago | |
| Monotonic | | | |
| quantreg | | | |
| rpart | | | |
| RuleFit | | | |
| Scalable Bayesian Rule Lists (SBRL) | | | |
| shapFlex | 71 | over 4 years ago | |
| shapleyR | 25 | over 5 years ago | |
| shapper | | | |
| smbinning | | | |
| vip | 187 | 11 months ago | |
| xgboostExplainer | 251 | over 6 years ago | |
awesome-machine-learning-interpretability 
jbhuang0604/awesome-computer-vision
lopusz/awesome-interpretable-machine-learning
datamllab/awesome-fairness-in-ai
0ex/more-awesome
radames/machine-learning-ethics-references