Awesome Machine Learning Interpretability 
A maintained and curated list of practical and awesome responsible machine learning resources.
If you want to contribute to this list (and please do!), read over the contribution guidelines, send a pull request, or file an issue.
If something you contributed or found here is missing after our September 2023 redeux, please check the archive.
Contents
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Community and Official Guidance Resources
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Education Resources
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Miscellaneous Resources
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Technical Resources
Community and Official Guidance Resources
Community Frameworks and Guidance
This section is for responsible ML guidance put forward by organizations or individuals, not for official government guidance.
- 8 Principles of Responsible ML
- A Brief Overview of AI Governance for Responsible Machine Learning Systems
- Adversarial ML Threat Matrix
- AI Verify:
- 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?
- BIML Interactive Machine Learning Risk Framework
- Center for Security and Emerging Technology (CSET):
- November 7, 2023, The Executive Order on Safe, Secure, and Trustworthy AI: Decoding Biden’s AI Policy Roadmap
- October 2023, Decoding Intentions: Artificial Intelligence and Costly Signals
- August 11, 2023, Understanding AI Harms: An Overview
- August 1, 2023, Large Language Models (LLMs): An Explainer
- July 21, 2023, Making AI (more) Safe, Secure, and Transparent: Context and Research from CSET
- July 2023, Adding Structure to AI Harm: An Introduction to CSET's AI Harm Framework
- June 2023, The Inigo Montoya Problem for Trustworthy AI: The Use of Keywords in Policy and Research
- June 2023, A Matrix for Selecting Responsible AI Frameworks
- March 2023, Reducing the Risks of Artificial Intelligence for Military Decision Advantage
- February 2023, One Size Does Not Fit All: Assessment, Safety, and Trust for the Diverse Range of AI Products, Tools, Services, and Resources
- January 2023, Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk
- October 2022, A Common Language for Responsible AI: Evolving and Defining DOD Terms for Implementation
- December 2021, AI and the Future of Disinformation Campaigns: Part 1: The RICHDATA Framework
- December 2021, AI and the Future of Disinformation Campaigns: Part 2: A Threat Model
- July 2021, AI Accidents: An Emerging Threat: What Could Happen and What to Do
- May 2021, Truth, Lies, and Automation: How Language Models Could Change Disinformation
- March 2021, Key Concepts in AI Safety: An Overview
- February 2021, Trusted Partners: Human-Machine Teaming and the Future of Military AI
- Censius: AI Audit
- Crowe LLP: Internal auditor's AI safety checklist
- DAIR Prompt Engineering Guide
- 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
- Ethical and social risks of harm from Language Models
- Evaluating LLMs is a minefield
- FATML Principles and Best Practices
- ForHumanity Body of Knowledge (BOK)
- The Foundation Model Transparency Index
- From Principles to Practice: An interdisciplinary framework to operationalise AI ethics
- Google:
- Google, Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
- Google, Data governance in the cloud - part 1 - People and processes
- Google, Data Governance in the Cloud - part 2 - Tools
- Google, Generative AI Prohibited Use Policy
- Google, Principles and best practices for data governance in the cloud
- Google, Responsible AI Framework
- Google, Responsible AI practices
- Google, Testing and Debugging in Machine Learning
- H2O.ai Algorithms
- Haptic Networks: How to Perform an AI Audit for UK Organisations
- Frontier Model Forum: What is Red Teaming?
- ICT Institute: A checklist for auditing AI systems
- IEEE:
- Independent Audit of AI Systems
- Identifying and Overcoming Common Data Mining Mistakes
- Infocomm Media Development Authority (Singapore), First of its kind Generative AI Evaluation Sandbox for Trusted AI by AI Verify Foundation and IMDA
- Institute of Internal Auditors: Artificial Intelligence Auditing Framework, Practical Applications, Part A, Special Edition
- ISACA:
- Large language models, explained with a minimum of math and jargon
- Larry G. Wlosinski, April 30, 2021, Information System Contingency Planning Guidance
- Llama 2 Responsible Use Guide
- Machine Learning Attack_Cheat_Sheet
- Machine Learning Quick Reference: Algorithms
- Machine Learning Quick Reference: Best Practices*
- Microsoft:
- NewsGuard AI Tracking Center
- Open Sourcing Highly Capable Foundation Models
- OpenAI Red Teaming Network
- Organization and Training of a Cyber Security Team
- PAI's Responsible Practices for Synthetic Media: A Framework for Collective Action (Partnership on AI)
- PwC's Responsible AI
- Real-World Strategies for Model Debugging
- RecoSense: Phases of an AI Data Audit – Assessing Opportunity in the Enterprise
- Red Teaming of Advanced Information Assurance Concepts
- Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
- Robust ML
- Safe and Reliable Machine Learning
- Sample AI Incident Response Checklist
- SHRM Generative Artificial Intelligence (AI) Chatbot Usage Policy
- Stanford University, Responsible AI at Stanford: Enabling innovation through AI best practices
- 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
- TechTarget: 9 questions to ask when auditing your AI systems
- Troubleshooting Deep Neural Networks
- Unite.AI: How to perform an AI Audit in 2023
- University of California, Berkeley, Information Security Office, How to Write an Effective Website Privacy Statement
- 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
- You Created A Machine Learning Application Now Make Sure It's Secure
Conferences and Workshops
This section is for conferences, workshops and other major events related to responsible ML.
- AAAI Conference on Artificial Intelligence
- ACM FAccT (Fairness, Accountability, and Transparency)
- 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)
- International Conference on Machine Learning (ICML)
- 2020:
- 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
- 2021:
- 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
- 2022:
- 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
- 2023:
- 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
- 2020:
- Knowledge, Discovery, and Data Mining (KDD)
- Neural Information Processing Systems (NeurIPs)
- 2022:
- 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
- 2023:
- 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
- Oxford Generative AI Summit Slides
- 2022:
Official Policy, Frameworks, and Guidance
This section serves as a repository for policy documents, regulations, guidelines, and recommendations that govern the ethical and responsible use of artificial intelligence and machine learning technologies. From international legal frameworks to specific national laws, the resources cover a broad spectrum of topics such as fairness, privacy, ethics, and governance.
- 12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)
- Aiming for truth, fairness, and equity in your company’s use of AI
- 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
- A Primer on Artificial Intelligence in Securities Markets
- Biometric Information Privacy Act
- Booker Wyden Health Care Letters
- California Consumer Privacy Act (CCPA)
- California Department of Justice, How to Read a Privacy Policy
- 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
- Congressional Research Service, Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th Congress
- Consumer Data Protection Act (Code of Virginia)
- 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, Frontier AI: capabilities and risks - discussion paper (United Kingdom)
- Department of Commerce, Intellectual property
- Department of Defense, AI Principles: Recommendations on the Ethical Use of Artificial Intelligence
- Department of Defense, Chief Data and Artificial Intelligence Officer (CDAO) Assessment and 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)
- EEOC Letter (from U.S. senators re: hiring software)
- European Commission, Hiroshima Process International Guiding Principles for Advanced AI system
- 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
- FTC Privacy Policy
- Government Accountability Office: Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities
- General Data Protection Regulation (GDPR)
- General principles for the use of Artificial Intelligence in the financial sector
- Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (France)
- 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
- 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), 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), NIST Special Publication 800-30 Revision 1, Guide for Conducting Risk Assessments
- National Science and Technology Council (NSTC), Select Committee on Artificial Intelligence, National Artificial Intelligence Research and Development Strategic Plan 2023 Update
- New York City Automated Decision Systems Task Force Report (November 2019)
- 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)
- 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
- 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)
- Supervisory Guidance on Model Risk Management
- Testing the Reliability, Validity, and Equity of Terrorism Risk Assessment Instruments
- UNESCO, Artificial Intelligence: examples of ethical dilemmas
- United States Department of Energy Artificial Intelligence and Technology Office:
- 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 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
Education Resources
Comprehensive Software Examples and Tutorials
This section is a curated collection of guides and tutorials that simplify responsible ML implementation. It spans from basic model interpretability to advanced fairness techniques. Suitable for both novices and experts, the resources cover topics like COMPAS fairness analyses and explainable machine learning via counterfactuals.
- 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
- Model Interpretation series by Dipanjan (DJ) Sarkar:
- Partial Dependence Plots in R
- PiML:
- Reliable-and-Trustworthy-AI-Notebooks
- Saliency Maps for Deep Learning
- Visualizing ML Models with LIME
- Visualizing and debugging deep convolutional networks
- What does a CNN see?
Free-ish Books
This section contains books that can be reasonably described as free, including some "historical" books dealing broadly with ethical and responsible tech.
- 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
- 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
- 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
- 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
Glossaries and Dictionaries
This section features a collection of glossaries and dictionaries that are geared toward defining terms in ML, including some "historical" dictionaries.
- A.I. For Anyone: The A-Z of AI
- Alan Turing Institute: Data science and AI glossary
- Appen Artificial Intelligence Glossary
- 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
- 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
- NIST AIRC: 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
- 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
Open-ish Classes
This section features a selection of educational courses focused on ethical considerations and best practices in ML. The classes range from introductory courses on data ethics to specialized training in fairness and trustworthy deep learning.
- An Introduction to Data Ethics
- 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
- 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
- Trustworthy Deep Learning
Miscellaneous Resources
AI Incident Information Sharing Resources
This section houses initiatives, networks, repositories, and publications that facilitate collective and interdisciplinary efforts to enhance AI safety. It includes platforms where experts and practitioners come together to share insights, identify potential vulnerabilities, and collaborate on developing robust safeguards for AI systems, including AI incident trackers.
- AI Incident Database (Responsible AI Collaborative)
- AI Vulnerability Database (AVID)
- AIAAIC
- George Washington University Law School's AI Litigation Database
- OECD AI Incidents Monitor
- Verica Open Incident Database (VOID)
Challenges and Competitions
This section contains challenges and competitions related to responsible ML.
- FICO Explainable Machine Learning Challenge
- National Fair Housing Alliance Hackathon
- Twitter Algorithmic Bias
Curated Bibliographies
We are seeking curated bibliographies related to responsible ML across various topics, see issue 115.
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BibTeX:
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Web:
List of Lists
This section links to other lists of responsible ML or related resources.
- A Living and Curated Collection of Explainable AI Methods
- AI Ethics Guidelines Global Inventory
- AI Ethics Resources
- AI Tools and Platforms
- Awesome interpretable machine learning
- Awesome-explainable-AI
- Awesome-ML-Model-Governance
- Awesome MLOps
- Awesome Production Machine Learning
- Awful AI
- criticalML
- Machine Learning Ethics References
- Machine Learning Interpretability Resources
- OECD-NIST Catalogue of AI Tools and Metrics
- OpenAI Cookbook
- private-ai-resources
- ResponsibleAI
- Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance
- XAI Resources
- xaience
Technical Resources
Benchmarks
This section contains benchmarks or datasets used for benchmarks for ML systems, particularly those related to responsible ML desiderata.
- benchm-ml
- Bias Benchmark for QA dataset (BBQ)
- HELM
- Nvidia MLPerf
- OpenML Benchmarking Suites
- TruthfulQA
- Winogender Schemas
- Real Toxicity Prompts (Allen Institute for AI)
Common or Useful Datasets
This section contains datasets that are commonly used in responsible ML evaulations or repositories of interesting/important data sources:
- Adult income dataset
- Balanced Faces in the Wild
- COMPAS Recidivism Risk Score Data and Analysis
- FANNIE MAE Single Family Loan Performance
- NYPD Stop, Question and Frisk Data
- Statlog (German Credit Data)
- Wikipedia Talk Labels: Personal Attacks
Domain-specific Software
This section curates specialized software tools aimed at responsible ML within specific domains, such as in healthcare, finance, or social sciences.
Machine Learning Environment Management Tools
This section contains open source or open access ML environment management software.
Open Source/Access Responsible AI Software Packages
This section contains open source or open access software used to implement responsible ML.
Browser
- BiasAware: Dataset Bias Detection
- DiscriLens
- manifold
- PAIR-code / facets
- TensorBoard Projector
- What-if Tool
C/C++
Python
- acd
- aequitas
- AI Fairness 360
- AI Explainability 360
- ALEPython
- Aletheia
- allennlp
- algofairness
- Alibi
- anchor
- Bayesian Case Model
- Bayesian Ors-Of-Ands
- Bayesian Rule List (BRL)
- BlackBoxAuditing
- casme
- Causal Discovery Toolbox
- captum
- causalml
- cdt15
- checklist
- cleverhans
- contextual-AI
- ContrastiveExplanation (Foil Trees)
- counterfit
- dalex
- debiaswe
- DeepExplain
- deeplift
- deepvis
- dianna
- DiCE
- DoWhy
- ecco
- eli5
- explabox
- Explainable Boosting Machine (EBM)/GA2M
- ExplainaBoard
- explainerdashboard
- explainX
- fair-classification
- fairml
- fairlearn
- fairness-comparison
- fairness_measures_code
- Falling Rule List (FRL)
- foolbox
- Giskard
- Grad-CAM (GitHub topic)
- gplearn
- H2O-3
- hate-functional-tests
- imodels
- iNNvestigate neural nets
- Integrated-Gradients
- interpret
- interpret_with_rules
- InterpretME
- Keras-vis
- keract
- L2X
- langtest
- learning-fair-representations
- lime
- LiFT
- lit
- lofo-importance
- lrp_toolbox
- MindsDB
- MLextend
- ml-fairness-gym
- ml_privacy_meter
- mllp
- Monotonic XGBoost
- Multilayer Logical Perceptron (MLLP)
- OptBinning
- Optimal Sparse Decision Trees
- parity-fairness
- PDPbox
- PiML-Toolbox
- Privacy-Preserving-ML
- ProtoPNet
- pyBreakDown
- PyCEbox
- pyGAM
- pymc3
- pySS3
- pytorch-innvestigate
- rationale
- responsibly
- revise-tool
- robustness
- RISE
- Risk-SLIM
- sage
- SALib
- Scikit-learn
- scikit-fairness
- scikit-multiflow
- shap
- shapley
- sklearn-expertsys
- skope-rules
- solas-ai-disparity
- Super-sparse Linear Integer models (SLIMs)
- tensorflow/lattice
- tensorflow/lucid
- tensorflow/fairness-indicators
- tensorflow/model-analysis
- tensorflow/model-card-toolkit
- tensorflow/model-remediation
- tensorflow/privacy
- tensorflow/tcav
- tensorfuzz
- TensorWatch
- TextFooler
- text_explainability
- text_sensitivity
- tf-explain
- Themis
- themis-ml
- TorchUncertainty
- treeinterpreter
- TRIAGE
- woe
- xai
- xdeep
- xplique
- yellowbrick
R
- ALEPlot
- arules
- Causal SVM
- DALEX
- DALEXtra
- DrWhyAI
- elasticnet
- ExplainPrediction
- Explainable Boosting Machine (EBM)/GA2M
- fairmodels
- fairness
- fastshap
- featureImportance
- flashlight
- forestmodel
- fscaret
- gam
- glm2
- glmnet
- H2O-3
- iBreakDown
- ICEbox
- iml
- ingredients
- intepret
- lightgbmExplainer
- lime
- live
- mcr
- modelDown
- modelOriented
- modelStudio
- Monotonic XGBoost
- quantreg
- rpart
- RuleFit
- Scalable Bayesian Rule Lists (SBRL)
- shapFlex
- shapleyR
- shapper
- smbinning
- vip
- xgboostExplainer
