Awesome-ML-Model-Governance

Model Governance Resources

A curated list of references and resources on Machine Learning Model Governance, Ethics, and Responsible AI for developers and data scientists.

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
Book: "Responsible AI". 2022. by Patrick Hall, Rumman Chowdhury. O'Reilly Media, Inc.
Book: "Practical Fairness". 2020. By Aileen Nielsen. O'Reilly Media, Inc.
Book: "Fairness and machine learning: Limitations and Opportunities." Barocas, S., Hardt, M. and Narayanan, A., 2018.
Book: "The Framework for ML Governance" by Kyle Gallatin. 2021. O'Reilly Media
What are model governance and model operations? A look at the landscape of tools for building and deploying robust, production-ready machine learning models
Specialized tools for machine learning development and model governance are becoming essential. Why companies are turning to specialized machine learning tools like MLflow.
What are model governance and model operations? – O’Reilly
AI Fairness 360, A Step Towards Trusted AI - IBM Research
Responsible AI
Learn how to integrate Responsible AI practices into your ML workflow using TensorFlow
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
Secure, privacy-preserving and federated machine learning in medical imaging
Explainable AI (Gartner Prediction for 2023)
What We've Learned to Control. By Ben Recht
Practical Data Ethics
"LiFT: A Scalable Framework for Measuring Fairness in ML Applications" Vasudevan, Sriram and Kenthapadi, Krishnaram. (2020) - Code:
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.
Philosophical grounding of AI fairness in Business Ethics
Data Ethics Canvas . Helps identify and manage ethical issues – at the start of a project that uses data, and throughout. Also see for broader scope
The Open Ethics Canvas by the Open Ethics 6 about 3 years ago
ABOUT ML Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles
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:
Navigate the road to Responsible AI – Gradient Flow Blog
😈 Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness 6,995 8 months ago
Seven legal questions for data scientists
2020 in Review: 8 New AI Regulatory Proposals from Governments
Model Governance resources 26 almost 4 years ago
ML Cards for D/MLOps Governance (The combination of code, data, model, and service cards for D/MLOps, as an integrated solution.)
To regulate AI, try playing in a sandbox
Biases in AI Systems. A survey for practitioners
Artificial Intelligence Incident Database
Data Ethics Considerations for more Responsible AI
Book: Interpretable Machine Learning with Python (by Serg Masis)
Fairness in Machine Learning
Paper: Hendrycks, Dan, Nicholas Carlini, John Schulman, and Jacob Steinhardt. "Unsolved problems in ml safety."(2021)

Security for ML
Cybersecurity for Data Science
Artifical intelligence and machine learning security (by Microsoft) The references therein are useful
Evtimov, Ivan, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, and Jerry Li. "Security and Machine Learning in the Real World." arXiv (2020).
Machine Learning Systems: Security
Enterprise Security and Governance MLOps (by Diego Oppenheimer)
Adversarial Machine Learning 101 1,056 over 1 year ago
ATLAS - Adversarial Threat Landscape for Artificial-Intelligence Systems 1,056 over 1 year ago

Reports
State of AI Ethics June 2020 Report by the Montreal AI Ethics Institute
State of AI Ethics October 2020 Report by the Montreal AI Ethics Institute
State of AI Ethics January 2021 Report by the Montreal AI Ethics Institute

Organizations
AI Ethics Impact Group: From Principles to Practice
Responsible AI Institute

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