- 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
- Vasudevan, Sriram and Kenthapadi, Krishnaram. "LiFT: A Scalable Framework for Measuring Fairness in ML Applications" (2020) - Code: The LinkedIn Fairness Toolkit (LiFT)
- 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.
- 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.
- The Open Ethics Canvas by the Open Ethics
- 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: Model Card Toolkit
- 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
- Seven legal questions for data scientists
- 2020 in Review: 8 New AI Regulatory Proposals from Governments
- Model Governance resources
- 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)
- 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
- ATLAS - Adversarial Threat Landscape for Artificial-Intelligence Systems