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This repository provides a curated list of references about Machine Learning Model Governance, Ethics, and Responsible AI.

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Awesome ML Model Governance

Model Governance, Ethics, Responsible AI

  1. Book: "Responsible AI". 2022. by Patrick Hall, Rumman Chowdhury. O'Reilly Media, Inc.
  2. Book: "Practical Fairness". 2020. By Aileen Nielsen. O'Reilly Media, Inc.
  3. Book: "Fairness and machine learning: Limitations and Opportunities." Barocas, S., Hardt, M. and Narayanan, A., 2018.
  4. Book: "The Framework for ML Governance" by Kyle Gallatin. 2021. O'Reilly Media
  5. What are model governance and model operations? A look at the landscape of tools for building and deploying robust, production-ready machine learning models
  6. Specialized tools for machine learning development and model governance are becoming essential. Why companies are turning to specialized machine learning tools like MLflow.
  7. What are model governance and model operations? – O’Reilly
  8. AI Fairness 360, A Step Towards Trusted AI - IBM Research
  9. Responsible AI
  10. Learn how to integrate Responsible AI practices into your ML workflow using TensorFlow
  11. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
  12. Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
  13. Secure, privacy-preserving and federated machine learning in medical imaging
  14. Explainable AI (Gartner Prediction for 2023)
  15. What We've Learned to Control. By Ben Recht
  16. Practical Data Ethics
  17. Vasudevan, Sriram and Kenthapadi, Krishnaram. "LiFT: A Scalable Framework for Measuring Fairness in ML Applications" (2020) - Code: The LinkedIn Fairness Toolkit (LiFT)
  18. Four Principles of Explainable Artificial Intelligence (NIST Draft). Phillips, P.J., Hahn, A.C., Fontana, P.C., Broniatowski, D.A. and Przybocki, M.A., 2020.
  19. Philosophical grounding of AI fairness in Business Ethics
  20. Data Ethics Canvas. Helps identify and manage ethical issues – at the start of a project that uses data, and throughout. Also see Ethics Canvas for broader scope.
  21. The Open Ethics Canvas by the Open Ethics
  22. ABOUT ML - Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles.
  23. Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit. "Model Cards for Model Reporting" (2019) - Code: Model Card Toolkit
  24. Navigate the road to Responsible AI – Gradient Flow Blog
  25. 😈 Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness
  26. Seven legal questions for data scientists
  27. 2020 in Review: 8 New AI Regulatory Proposals from Governments
  28. Model Governance resources
  29. ML Cards for D/MLOps Governance (The combination of code, data, model, and service cards for D/MLOps, as an integrated solution.)
  30. To regulate AI, try playing in a sandbox
  31. Biases in AI Systems. A survey for practitioners
  32. Artificial Intelligence Incident Database
  33. Data Ethics Considerations for more Responsible AI
  34. Book: Interpretable Machine Learning with Python (by Serg Masis)
  35. Fairness in Machine Learning
  36. Paper: Hendrycks, Dan, Nicholas Carlini, John Schulman, and Jacob Steinhardt. "Unsolved problems in ml safety."(2021)

Security for ML

  1. Cybersecurity for Data Science
  2. Artifical intelligence and machine learning security (by Microsoft) The references therein are useful.
  3. Evtimov, Ivan, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, and Jerry Li. "Security and Machine Learning in the Real World." arXiv (2020).
  4. Machine Learning Systems: Security
  5. Enterprise Security and Governance MLOps (by Diego Oppenheimer)
  6. Adversarial Machine Learning 101
  7. ATLAS - Adversarial Threat Landscape for Artificial-Intelligence Systems

Reports

  1. State of AI Ethics June 2020 Report by the Montreal AI Ethics Institute
  2. State of AI Ethics October 2020 Report by the Montreal AI Ethics Institute
  3. State of AI Ethics January 2021 Report by the Montreal AI Ethics Institute

Organizations

  1. AI Ethics Impact Group: From Principles to Practice
  2. Responsible AI Institute

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