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awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
https://github.com/jphall663/awesome-machine-learning-interpretability
- 8 Principles of Responsible ML
- A Brief Overview of AI Governance for Responsible Machine Learning Systems
- Acceptable Use Policies for Foundation Models
- Ada Lovelace Institute, Code and Conduct: How to Create Third-Party Auditing Regimes for AI Systems
- Adversarial ML Threat Matrix
- AI Governance Needs Sociotechnical Expertise: Why the Humanities and Social Sciences Are Critical to Government Efforts
- 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
- AI Snake Oil
- 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
- CSET's Harm Taxonomy for the AI Incident Database - cset/CSET-AIID-harm-taxonomy?style=social)
- 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
- Censius: AI Audit
- Center for AI and Digital Policy Reports
- Center for Democracy and Technology (CDT), Applying Sociotechnical Approaches to AI Governance in Practice
- 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
- 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
- Evaluating LLMs is a minefield
- Fairly's Global AI Regulations Map - ai-regulations-map?style=social)
- FATML Principles and Best Practices
- ForHumanity Body of Knowledge (BOK)
- The Foundation Model Transparency Index
- Trustible, Model Transparency Ratings
- From Principles to Practice: An interdisciplinary framework to operationalise AI ethics
- The Future Society
- Gage Repeatability and Reproducibility
- Georgetown University Library's Artificial Intelligence (Generative) Resources
- 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
- H2O.ai Algorithms - tutorials?style=social)
- 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
- ICT Institute: A checklist for auditing AI systems
- IEEE P3119 Standard for the Procurement of Artificial Intelligence and Automated Decision Systems
- IEEE Std 1012-1998 Standard for Software Verification and Validation
- 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
- ISACA: Auditing Artificial Intelligence
- ISACA: Auditing Guidelines for Artificial Intelligence
- ISACA: Capability Maturity Model Integration Resources
- Integrity Institute Report, February 2024, On Risk Assessment and Mitigation for Algorithmic Systems
- Know Your Data
- 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 - ai-framework?style=social)
- Llama 2 Responsible Use Guide
- LLM Visualization
- Machine Learning Quick Reference: Algorithms
- Machine Learning Quick Reference: Best Practices
- Manifest MLBOM Wiki
- Towards Traceability in Data Ecosystems using a Bill of Materials Model
- System cards
- Azure AI Content Safety
- Harm categories in Azure AI Content Safety
- Microsoft Responsible AI Standard, v2
- GDPR and Generative AI: A Guide for Public Sector Organizations
- MLA, How do I cite generative AI in MLA style?
- model-cards-and-datasheets - cards-and-datasheets?style=social)
- NewsGuard AI Tracking Center
- OECD.AI, The Bias Assessment Metrics and Measures Repository
- OpenAI, Building an early warning system for LLM-aided biological threat creation
- OpenAI Cookbook, How to implement LLM guardrails
- OpenAI, Evals
- Open Sourcing Highly Capable Foundation Models
- Organization and Training of a Cyber Security Team
- Our Data Our Selves, Data Use Policy
- 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, 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, 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
- World Privacy Forum, Risky Analysis: Assessing and Improving AI Governance Tools
- You Created A Machine Learning Application Now Make Sure It's Secure
- 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
- 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
- Machine Learning Attack_Cheat_Sheet
- Oliver Patel's Cheat Sheets
- 10 Key Pillars for Enterprise AI Governance
- 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
- 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
- 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
- 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
- No, LLM Agents can not Autonomously Exploit One-day Vulnerabilities
- 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 - analyst?style=social)
- 0xk1h0 / ChatGPT "DAN" (and other "Jailbreaks")
- ACL 2024 Tutorial: Vulnerabilities of Large Language Models to Adversarial Attacks
- Azure's PyRIT
- 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
- coolaj86 / Chat GPT "DAN" (and other "Jailbreaks")
- CSET, What Does AI-Red Teaming Actually Mean?
- DAIR Prompt Engineering Guide
- DAIR Prompt Engineering Guide GitHub - ai/Prompt-Engineering-Guide?style=social)
- 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
- LLM Security & Privacy - sp?style=social)
- Membership Inference Attacks and Defenses on Machine Learning Models Literature - inference-machine-learning-literature?style=social)
- Learn Prompting, Prompt Hacking
- MiesnerJacob / learn-prompting, Prompt Hacking - prompting?style=social)
- Lakera AI's Gandalf
- leondz / garak
- Microsoft AI Red Team building future of safer AI
- OpenAI Red Teaming Network
- r/ChatGPTJailbreak
- developer mode fixed
- Y Combinator, ChatGPT Grandma Exploit
- AAAI Conference on Artificial Intelligence
- ACM FAccT (Fairness, Accountability, and Transparency)
- FAT/ML (Fairness, Accountability, and Transparency in Machine Learning)
- 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)
- 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
- Knowledge, Discovery, and Data Mining (KDD)
- 2nd ACM SIGKDD Workshop on Ethical Artificial Intelligence: Methods and Applications
- KDD Data Science for Social Good 2023
- Mission Control AI, Booz Allen Hamilton, and The Intellectual Forum at Jesus College, Cambridge, The 2024 Leaders in Responsible AI Summit, March 22, 2024
- Neural Information Processing Systems (NeurIPs)
- 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
- OECD.AI, Building the foundations for collaboration: The OECD-African Union AI Dialogue
- Oxford Generative AI Summit Slides
- 12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)
- Aiming for truth, fairness, and equity in your company’s use of AI
- AI Safety Institute (AISI), Advanced AI evaluations at AISI: May update
- Algorithmic Accountability Act of 2023
- Algorithm Charter for Aotearoa New Zealand
- A Regulatory Framework for AI: Recommendations for PIPEDA Reform
- Artificial Intelligence (AI) in the Securities Industry
- Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment - Shaping Europe’s digital future - European Commission
- Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology
- Autoriteit Persoonsgegevens, scraping bijna altijd illegal (Dutch Data Protection Authority, "Scraping is always illegal")
- Biometric Information Privacy Act
- Booker Wyden Health Care Letters
- Bundesamt für Sicherheit in der Informationstechnik, Generative AI Models - Opportunities and Risks for Industry and Authorities
- 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)
- Can’t lose what you never had: Claims about digital ownership and creation in the age of generative AI
- Children's Online Privacy Protection Rule ("COPPA")
- Civil liability regime for artificial intelligence
- Colorado General Assembly, SB24-205 Consumer Protections for Artificial Intelligence, Concerning consumer protections in interactions with artificial intelligence systems"
- Commodity Futures Trading Commission (CFTC), A Primer on Artificial Intelligence in Securities Markets
- Commodity Futures Trading Commission (CFTC), Responsible Artificial Intelligence in Financial Markets
- Congressional Research Service, Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th Congress
- Consumer Data Protection Act (Code of Virginia)
- Consumer Financial Protection Bureau (CFPB), Chatbots in consumer finance
- DARPA, Explainable Artificial Intelligence (XAI) (Archived)
- Data Availability and Transparency Act 2022 (Australia)
- data.gov, Privacy Policy and Data Policy
- Defense Technical Information Center, Computer Security Technology Planning Study, October 1, 1972
- De-identification Tools
- 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
- Developing Financial Sector Resilience in a Digital World: Selected Themes in Technology and Related Risks
- The Digital Services Act package (EU Digital Services Act and Digital Markets Act)
- Directive on Automated Decision Making (Canada)
- (Draft Guideline) E-23 – Model Risk Management
- EEOC Letter (from U.S. senators re: hiring software)
- European Commission, Hiroshima Process International Guiding Principles for Advanced AI system
- European Data Protection Supervisor, First EDPS Orientations for EUIs using Generative AI
- European Parliament, Addressing AI risks in the workplace: Workers and algorithms
- 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
- FDA Artificial Intelligence/Machine Learning (AI/ML)-Based: Software as a Medical Device (SaMD) Action Plan, updated January 2021
- FDA Software as a Medical Device (SAMD) guidance (December 8, 2017)
- FDIC Supervisory Guidance on Model Risk Management
- Federal Consumer Online Privacy Rights Act (COPRA)
- Federal Reserve Bank of Dallas, Regulation B, Equal Credit Opportunity, Credit Scoring Interpretations: Withdrawl of Proposed Business Credit Amendments, June 3, 1982
- FHA model risk management/model governance guidance
- FTC 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
- FTC Privacy Policy
- Government Accountability Office: Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities
- General Data Protection Regulation (GDPR)
- Article 22 EU GDPR "Automated individual decision-making, including profiling"
- General principles for the use of Artificial Intelligence in the financial sector
- Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (France)
- Guidelines for secure AI system development
- IAPP Global AI Legislation Tracker
- IAPP US State Privacy Legislation Tracker
- Innovation spotlight: Providing adverse action notices when using AI/ML models
- Justice in Policing Act
- Mississippi Department of Education, Artificial Intelligence Guidance for K-12 Classrooms
- National Conference of State Legislatures (NCSL) 2020 Consumer Data Privacy Legislation
- National Institute of Standards and Technology (NIST), AI 100-1 Artificial Intelligence Risk Management Framework (NIST AI RMF 1.0)
- 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), Assessing Risks and Impacts of AI (ARIA)
- National Institute of Standards and Technology (NIST), Four Principles of Explainable Artificial Intelligence, Draft NISTIR 8312, 2020-08-17
- National Institute of Standards and Technology (NIST), Four Principles of Explainable Artificial Intelligence, NISTIR 8312, 2021-09-29
- National Institute of Standards and Technology (NIST), Engineering Statistics Handbook
- 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
- National Institute of Standards and Technology (NIST), NIST Special Publication 800-30 Revision 1, Guide for Conducting Risk Assessments
- National Physical Laboratory (NPL), Beginner's guide to measurement GPG118
- National Security Agency, Central Security Service, Artificial Intelligence Security Center
- National Security Commission on Artificial Intelligence, Final Report
- National Science and Technology Council (NSTC), Select Committee on Artificial Intelligence, National Artificial Intelligence Research and Development Strategic Plan 2023 Update
- National Telecommunications and Information Administration, AI Accountability Policy Report
- NATO, Narrative Detection and Topic Modelling in the Baltics
- New York City Automated Decision Systems Task Force Report (November 2019)
- OECD, Open, Useful and Re-usable data (OURdata) Index: 2019 - Policy Paper
- Office of the Director of National Intelligence (ODNI), The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines
- Office of Management and Budget, Guidance for Regulation of Artificial Intelligence Applications, finalized November 2020
- Office of Science and Technology Policy, Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People
- Office of the Comptroller of the Currency (OCC), 2021 Model Risk Management Handbook
- Online Harms White Paper: Full government response to the consultation (United Kingdom)
- Online Privacy Act of 2023
- Online Safety Bill (United Kingdom)
- Principles of Artificial Intelligence Ethics for the Intelligence Community
- Privacy Act 1988 (Australia)
- 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
- Psychological Foundations of Explainability and Interpretability in Artificial Intelligence
- The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018 (United Kingdom)
- Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures
- Questions from the Commission on Protecting Privacy and Preventing Discrimination
- RE: Use of External Consumer Data and Information Sources in Underwriting for Life Insurance
- Securities and Exchange Commission, SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence
- Singapore's Companion to the Model AI Governance Framework – Implementation and Self-Assessment Guide for Organizations
- Singapore's Compendium of Use Cases: Practical Illustrations of the Model AI Governance Framework
- Singapore's Model Artificial Intelligence Governance Framework (Second Edition)
- State of California, Department of Technology, Office of Information Security, Generative Artificial Intelligence Risk Assessment, SIMM 5305-F, March 2024
- Supervisory Guidance on Model Risk Management
- Testing the Reliability, Validity, and Equity of Terrorism Risk Assessment Instruments
- UNESCO, Artificial Intelligence: examples of ethical dilemmas
- Office of the United Nations High Commissioner for Human Rights
- United States Department of Commerce, Intellectual property
- United States Department of Commerce Internet Policy Task Force, Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy Framework
- United States Department of Defense, AI Principles: Recommendations on the Ethical Use of Artificial Intelligence
- United States Department of Defense, Chief Data and Artificial Intelligence Officer (CDAO) Assessment and Assurance
- RAI Toolkit
- United States Department of Energy Artificial Intelligence and Technology Office
- DOE AI Risk Management Playbook (AIRMP)
- DOE AI Use Case Inventory (DOE Use Cases Releasable to Public in Accordance with E.O. 13960)
- DOE Digital Climate Solutions Inventory
- United States Department of Homeland Security, Safety and Security Guidelines for Critical Infrastructure Owners and Operators
- United States Department of Homeland Security, Use of Commercial Generative Artificial Intelligence (AI) Tools
- United States Department of Justice, Privacy Act of 1974
- United States Department of Justice, Overview of The Privacy Act of 1974 (2020 Edition)
- United States Department of the Treasury, Managing Artificial Intelligence-Specific Cybersecurity Risks in the Financial Services Sector, March 2024
- United States Patent and Trademark Office (USPTO), Public Views on Artificial Intelligence and Intellectual Property Policy
- Using Artificial Intelligence and Algorithms
- U.S. Army Concepts Analysis Agency, Proceedings of the Thirteenth Annual U.S. Army Operations Research Symposium, Volume 1, October 29 to November 1, 1974
- U.S. Web Design System (USWDS) Design principles
- The White House, Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy, February 2012
- COMPAS Analysis Using Aequitas
- Explaining Quantitative Measures of Fairness (with SHAP)
- 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
- Interpreting Machine Learning Models with the iml Package
- Interpretable Machine Learning using Counterfactuals
- Machine Learning Explainability by Kaggle Learn
- 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
- Partial Dependence Plots in R
- PiML Medium Tutorials
- PiML-Toolbox Examples - Toolbox?style=social)
- Reliable-and-Trustworthy-AI-Notebooks - and-Trustworthy-AI-Notebooks?style=social)
- Saliency Maps for Deep Learning
- Visualizing ML Models with LIME
- Visualizing and debugging deep convolutional networks
- What does a CNN see?
- 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*
- christophM/interpretable-ml-book - ml-book?style=social)
- 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
- George Reynolds, 2002, *Ethics in Information Technology*
- George Reynolds, 2002, *Ethics in Information Technology*, Instructor's Edition
- 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*
- Patrick Hall, James Curtis, Parul Pandey, and Agus Sudjianto, 2023, *Machine Learning for High-Risk Applications: Approaches to Responsible AI*
- 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*
- Soraj Hongladarom and Charles Ess, 2007, *Information Technology Ethics: Cultural Perspectives*
- 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
- A.I. For Anyone: The A-Z of AI
- 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
- ISO: 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
- Stanford University HAI Artificial Intelligence Definitions
- TechTarget: Artificial intelligence glossary: 60+ terms to know
- TELUS International: 50 AI terms every beginner should know
- 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
- An Introduction to Data Ethics
- 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
- ETH Zürich ReliableAI 2022 Course Project repository - Trustworthy-AI?style=social)
- Fairness in Machine Learning
- Fast.ai Data Ethics course
- Human-Centered Machine Learning
- Introduction to AI Ethics
- INFO 4270: Ethics and Policy in Data Science
- Introduction to Responsible Machine Learning
- 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
- Internet of Bugs
- Tech Won't Save Us
- AI Incident Database (Responsible AI Collaborative)
- AI Vulnerability Database (AVID)
- AIAAIC
- AI Badness: An open catalog of generative AI badness
- AI Risk Database
- 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)
- FICO Explainable Machine Learning Challenge
- OSD Bias Bounty
- National Fair Housing Alliance Hackathon
- Twitter Algorithmic Bias
- Against predictive optimization
- AI chatbots use racist stereotypes even after anti-racism training
- AI Is a Lot of Work
- AI Safety Is a Narrative Problem
- AI Snake Oil
- AI Tools Still Permitting Political Disinfo Creation, NGO Warns
- Are Emergent Abilities of Large Language Models a Mirage?
- Artificial Hallucinations in ChatGPT: Implications in Scientific Writing
- Artificial intelligence and illusions of understanding in scientific research
- ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
- Aylin Caliskan's publications
- 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 - 7.pdf)
- The Data Scientific Method vs. The Scientific Method
- Ed Zitron's Where's Your Ed At
- 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
- Generative AI’s environmental costs are soaring — and mostly secret
- 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
- 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)
- 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
- Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
- Measuring the predictability of life outcomes with a scientific mass collaboration
- The mechanisms of AI hype and its planetary and social costs
- 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
- 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’
- Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer 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
- Sustainable AI: Environmental Implications, Challenges and Opportunities
- 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.
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- Vox, AI already uses as much energy as a small country. It’s only the beginning.
- 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
- Center for AI and Digital Policy
- Institute for Advanced Study (IAS), AI Policy and Governance Working Group
- Blair Attard-Frost, INF1005H1S: Artificial Intelligence Policy Supplementary Reading List
- LLM Security & Privacy - sp?style=social)
- Membership Inference Attacks and Defenses on Machine Learning Models Literature - inference-machine-learning-literature?style=social)
- Proposed Guidelines for Responsible Use of Explainable Machine Learning (presentation, bibliography)
- Proposed Guidelines for Responsible Use of Explainable Machine Learning (paper, bibliography)
- A Responsible Machine Learning Workflow (paper, bibliography) - information-2019?style=social)
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
- A Living and Curated Collection of Explainable AI Methods
- AI Ethics Guidelines Global Inventory
- AI Ethics Resources
- AI Tools and Platforms
- Awesome AI Guidelines - artificial-intelligence-guidelines?style=social)
- Awesome interpretable machine learning - interpretable-machine-learning?style=social)
- Awesome-explainable-AI - ntu/Awesome-explainable-AI?style=social)
- Awesome-ML-Model-Governance - ML-Model-Governance?style=social)
- Awesome MLOps - mlops?style=social)
- Awesome Production Machine Learning - machine-learning-operations?style=social)
- Awful AI - ai?style=social)
- Casey Fiesler's AI Ethics & Policy News spreadsheet
- criticalML
- Ethics for people who work in tech
- Evaluation Repository for 'Sociotechnical Safety Evaluation of Generative AI Systems'
- IMDA-BTG, LLM-Evals-Catalogue - BTG/LLM-Evals-Catalogue?style=social)
- Machine Learning Ethics References - Learning-Ethics-References?style=social)
- Machine Learning Interpretability Resources - resources?style=social)
- OECD-NIST Catalogue of AI Tools and Metrics
- OpenAI Cookbook - cookbook?style=social)
- private-ai-resources - ai-resources?style=social)
- Ravit Dotan's Resources
- ResponsibleAI
- Tech & Ethics Curricula
- Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance
- XAI Resources
- xaience
- benchm-ml - 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.)." |
- Bias Benchmark for QA dataset (BBQ) - mll/bbq?style=social) | "Repository for the Bias Benchmark for QA dataset." |
- Cataloguing LLM Evaluations - 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." |
- DecodingTrust
- EleutherAI, Language Model Evaluation Harness - evaluation-harness?style=social) | "A framework for few-shot evaluation of language models." |
- GEM
- HELM
- Hugging Face, evaluate
- i-gallegos, Fair-LLM-Benchmark - gallegos/Fair-LLM-Benchmark?style=social) | Benchmark from "Bias and Fairness in Large Language Models: A Survey" |
- 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
- SafetyPrompts.com
- Sociotechnical Safety Evaluation Repository
- TrustLLM-Benchmark
- Trust-LLM-Benchmark Leaderboard
- TruthfulQA
- WAVES: Benchmarking the Robustness of Image Watermarks
- Winogender Schemas - schemas?style=social) | "Data for evaluating gender bias in coreference resolution systems." |
- Real Toxicity Prompts (Allen Institute for AI)
- Adult income dataset
- Balanced Faces in the Wild - bias-bfw?style=social)
- Bruegel, A dataset on EU legislation for the digital world
- 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
- FANNIE MAE Single Family Loan Performance
- nikhgarg / EmbeddingDynamicStereotypes
- Presidential Deepfakes Dataset
- NYPD Stop, Question and Frisk Data
- socialfoundations / folktables
- Statlog (German Credit Data)
- Wikipedia Talk Labels: Personal Attacks
- CometLLM
- dvc
- gigantum - driven science." |
- mlflow
- mlmd - metadata?style=social) | "For recording and retrieving metadata associated with ML developer and data scientist workflows." |
- modeldb
- neptune
- DiscriLens
- Hugging Face, BiasAware: Dataset Bias Detection
- manifold - agnostic visual debugging tool for machine learning." |
- PAIR-code / datacardsplaybook - code/datacardsplaybook?style=social) | "The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation." |
- PAIR-code / facets - code/facets?style=social) | "Visualizations for machine learning datasets." |
- PAIR-code / knowyourdata - 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." |
- TensorBoard Projector
- What-if Tool
- Born-again Tree Ensembles - 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)." |
- Certifiably Optimal RulE ListS
- Secure-ML - 28/Secure-ML?style=social) | "Secure Linear Regression in the Semi-Honest Two-Party Setting." |
- LDNOOBW
- acd
- aequitas - 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.” |
- AI Fairness 360 - 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.” |
- AI Explainability 360
- ALEPython - ai/ALEPython?style=social) | "Python Accumulated Local Effects package.” |
- Aletheia
- allennlp - source NLP research library, built on PyTorch.” |
- algofairness
- Alibi - quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.” |
- anchor - Precision Model-Agnostic Explanations' paper.” |
- Bayesian Case Model
- Bayesian Ors-Of-Ands - of-and algorithm as described in the BOA paper. We include the tictactoe dataset in the correct formatting to be used by this code.” |
- Bayesian Rule List (BRL)
- BlackBoxAuditing - learning models.” |
- CalculatedContent, WeightWatcher
- casme - agnostic saliency map extraction, a practical algorithm to train a classifier-agnostic saliency mapping by simultaneously training a classifier and a saliency mapping.” |
- Causal Discovery Toolbox
- captum
- causalml
- cdt15, Causal Discovery Lab., Shiga University - Gaussianity of the data." |
- checklist
- cleverhans - lab/cleverhans?style=social) | "An adversarial example library for constructing attacks, building defenses, and benchmarking both.” |
- contextual-AI - 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.” |
- ContrastiveExplanation (Foil Trees)
- counterfit
- dalex
- debiaswe - b/debiaswe?style=social) | "Remove problematic gender bias from word embeddings.” |
- DeepExplain - 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.” |
- DeepLIFT - used methods such as gradients, gradient-times-input (equivalent to a version of Layerwise Relevance Propagation for ReLU networks), guided backprop and integrated gradients.” |
- deepvis - 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.” |
- 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.” |
- DiCE
- DoWhy
- dtreeviz
- ecco - based language models (like GPT2, BERT, RoBERTA, T5, and T0).” |
- eli5 - Memex/eli5?style=social) | "A library for debugging/inspecting machine learning classifiers and explaining their predictions.” |
- explabox - house or acquired externally. The explabox turns your ingestibles (AI/ML model and/or dataset) into digestibles (statistics, explanations or sensitivity insights).” |
- Explainable Boosting Machine (EBM)/GA2M - 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.” |
- ExplainaBoard
- explainerdashboard - called "blackbox" machine learning models.” |
- explainX
- fair-classification - classification?style=social) | "Python code for training fair logistic regression classifiers.” |
- fairml
- fairlearn
- fairness-comparison - comparison?style=social) | "meant to facilitate the benchmarking of fairness aware machine learning algorithms.” |
- fairness_measures_code
- Falling Rule List (FRL)
- foolbox
- 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.” |
- Grad-CAM - 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. |
- gplearn - learn inspired and compatible API.” |
- H2O-3 - stable/h2o-r/docs/reference/h2o.glrm.html) | "Builds a generalized low rank decomposition of an H2O data frame." |
- H2O-3 - stable/h2o-r/docs/reference/h2o.glrm.html) | "Builds a generalized low rank decomposition of an H2O data frame." |
- H2O-3 - stable/h2o-r/docs/reference/h2o.glrm.html) | "Builds a generalized low rank decomposition of an H2O data frame." |
- h2o-LLM-eval - LLM-eval?style=social) | "Large-language Model Evaluation framework with Elo Leaderboard and A-B testing." |
- hate-functional-tests - 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. |
- imodels - compatible and easy to use.” |
- iNNvestigate neural nets
- Integrated-Gradients - 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).” |
- interpret - source package that incorporates state-of-the-art machine learning interpretability techniques under one roof.” |
- interpret_with_rules
- InterpretME - 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.” |
- Keras-vis - vis?style=social) | "a high-level toolkit for visualizing and debugging your trained keras neural net models.” |
- keract
- L2X - 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.” |
- langtest
- learning-fair-representations - fair-representations?style=social) | "Python numba implementation of Zemel et al. 2013 <http://www.cs.toronto.edu/~toni/Papers/icml-final.pdf>"
- lilac
- lime
- LiFT - 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.” |
- lit - 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.” |
- lofo-importance - 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.” |
- lrp_toolbox - 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.” |
- MindsDB - time data to perform their tasks.” |
- MLextend - to-day data science tasks.” |
- ml-fairness-gym - 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.” |
- ml_privacy_meter - 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.” |
- mllp
- Monotonic Constraints
- XGBoost
- Multilayer Logical Perceptron (MLLP)
- OptBinning - 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.” |
- Optimal Sparse Decision Trees
- parity-fairness
- PDPbox
- PiML-Toolbox - 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.” |
- Privacy-Preserving-ML - 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.” |
- ProtoPNet - 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).” |
- pyBreakDown
- PyCEbox
- pyGAM
- pymc3 - 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.” |
- pySS3
- pytorch-grad-cam - 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.” |
- pytorch-innvestigate - innvestigate?style=social) | "PyTorch implementation of Keras already existing project: [https://github.com/albermax/innvestigate/](https://github.com/albermax/innvestigate/).” |
- Quantus - machine-intelligence-lab/Quantus?style=social) | "Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations." |
- rationale
- responsibly
- REVISE: REvealing VIsual biaSEs - 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.” |
- robustness - lab.ml/)) created to make training, evaluating, and exploring neural networks flexible and easy.” |
- RISE - 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).” |
- Risk-SLIM - SLIM?style=social) | "a machine learning method to fit simple customized risk scores in python.” |
- SAGE - 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.” |
- SALib
- Scikit-Explain - friendly Python module for machine learning explainability," featuring PD and ALE plots, LIME, SHAP, permutation importance and Friedman's H, among other methods. |
- Scikit-learn - learn.org/stable/modules/tree.html) | "a non-parametric supervised learning method used for classification and regression.” |
- Scikit-learn - 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.” |
- Scikit-learn - 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.” |
- scikit-fairness - fairness?style=social) | Historical link. Merged with [fairlearn](https://fairlearn.org/). |
- scikit-multiflow
- shap
- shapley
- sklearn-expertsys - 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.” |
- skope-rules - 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.” |
- solas-ai-disparity - 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.” |
- Super-sparse Linear Integer models (SLIMs) - python?style=social) | "a package to learn customized scoring systems for decision-making problems.” |
- tensorflow/lattice - Up Tables in TensorFlow.” |
- tensorflow/lucid
- tensorflow/fairness-indicators - indicators?style=social) | "designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader Tensorflow toolkit.” |
- tensorflow/model-analysis - 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.” |
- tensorflow/model-card-toolkit - 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.” |
- tensorflow/model-remediation - 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.” |
- tensorflow/privacy
- tensorflow/tcav
- tensorfuzz - research/tensorfuzz?style=social) | "a library for performing coverage guided fuzzing of neural networks.” |
- TensorWatch - time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.” |
- TextFooler
- text_explainability - known state-of-the-art explainability approaches for text can be composed.” |
- text_sensitivity
- tf-explain - explain?style=social) | "Implements interpretability methods as Tensorflow 2.x callbacks to ease neural network's understanding.” |
- Themis - UMASS/Themis?style=social) | "A testing-based approach for measuring discrimination in a software system.” |
- themis-ml - ml?style=social) | "A Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms.” |
- TorchUncertainty - U2IS/torch-uncertainty?style=social) | "A package designed to help you leverage uncertainty quantification techniques and make your deep neural networks more reliable.” |
- treeinterpreter - learn's decision tree and random forest predictions.” |
- TRIAGE - Centric AI" framework for data characterization tailored for regression.” |
- woe
- xai
- xdeep
- xplique - 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.” |
- ydata-profiling - profiling?style=social) | "Provide[s] a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution.” |
- yellowbrick - learn API to allow human steering of the model selection process.” |
- ALEPlot - order interaction effects in black-box supervised learning models." |
- arules
- Causal SVM
- DALEX
- DALEXtra: Extension for 'DALEX' Package
- DrWhyAI
- elasticnet - Net and also provides functions for doing sparse PCA." |
- ExplainPrediction
- Explainable Boosting Machine (EBM)/GA2M
- fairmodels
- fairness
- fastshap
- featureImportance - agnostic manner." |
- flashlight
- forestmodel
- fscaret
- gam
- glm2
- glmnet - net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression." |
- H2O-3 - stable/h2o-r/docs/reference/h2o.glrm.html) | "Builds a generalized low rank decomposition of an H2O data frame." |
- H2O-3 - stable/h2o-r/docs/reference/h2o.glrm.html) | "Builds a generalized low rank decomposition of an H2O data frame." |
- H2O-3 - stable/h2o-r/docs/reference/h2o.glrm.html) | "Builds a generalized low rank decomposition of an H2O data frame." |
- iBreakDown
- ICEbox: Individual Conditional Expectation Plot Toolbox
- iml
- ingredients
- interpret: Fit Interpretable Machine Learning Models
- lightgbmExplainer
- lime
- live
- mcr
- modelDown
- modelOriented - based MI².AI. |
- modelStudio
- Monotonic
- quantreg
- rpart
- RuleFit
- Scalable Bayesian Rule Lists (SBRL)
- shapFlex
- shapleyR
- shapper
- smbinning
- vip
- xgboostExplainer
Programming Languages
Keywords
machine-learning
65
interpretability
28
explainable-ai
24
python
23
xai
21
data-science
20
interpretable-machine-learning
19
explainable-ml
17
deep-learning
16
fairness
15
ai
13
artificial-intelligence
12
interpretable-ai
10
bias
9
responsible-ai
8
tensorflow
8
transparency
8
interpretable-ml
8
visualization
8
iml
8
r
7
scikit-learn
7
explainability
6
shapley
6
discrimination
6
pytorch
6
machine-learning-interpretability
5
natural-language-processing
5
nlp
5
fairness-ml
5
fairness-ai
5
data-mining
5
explainable-artificial-intelligence
5
ml
5
privacy
4
feature-importance
4
causal-inference
4
llm
4
random-forest
4
xgboost
4
reliable-ai
4
h2o
4
spark
3
jupyter-notebook
3
data-analysis
3
keras
3
interpretable-deep-learning
3
chatgpt
3
model-visualization
3
responsible-ml
3