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Awesome-ML-Model-Governance
This repository provides a curated list of references about Machine Learning Model Governance, Ethics, and Responsible AI.
https://github.com/visenger/Awesome-ML-Model-Governance
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Model Governance, Ethics, Responsible AI
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- 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
- Book: "Responsible AI". 2022. by Patrick Hall, Rumman Chowdhury. O'Reilly Media, Inc.
- Book: "Practical Fairness". 2020. By Aileen Nielsen. O'Reilly Media, Inc.
- Specialized tools for machine learning development and model governance are becoming essential. Why companies are turning to specialized machine learning tools like MLflow.
- AI Fairness 360, A Step Towards Trusted AI - IBM Research
- 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" - Code: [The LinkedIn Fairness Toolkit (LiFT)](https://github.com/linkedin/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
- 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](https://github.com/tensorflow/model-card-toolkit)
- Seven legal questions for data scientists
- 2020 in Review: 8 New AI Regulatory Proposals from Governments
- 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)
- 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
- 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
- AI Ethics Impact Group: From Principles to Practice
- Responsible AI Institute
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- ML Cards for D/MLOps Governance (The combination of code, data, model, and service cards for D/MLOps, as an integrated solution.)
- Data Ethics Considerations for more Responsible AI
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- The Open Ethics Canvas by the Open Ethics
- ATLAS - Adversarial Threat Landscape for Artificial-Intelligence Systems
- Model Governance resources
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Paper: Hendrycks, Dan, Nicholas Carlini, John Schulman, and Jacob Steinhardt. "Unsolved problems in ml safety."(2021)
- Evtimov, Ivan, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, and Jerry Li. "Security and Machine Learning in the Real World." arXiv (2020).
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Enterprise Security and Governance MLOps (by Diego Oppenheimer)
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- What are model governance and model operations? – O’Reilly
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- 😈 Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Navigate the road to Responsible AI – Gradient Flow Blog
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Responsible AI
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- AI Fairness 360, A Step Towards Trusted AI - IBM Research
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
- Philosophical grounding of AI fairness in Business Ethics
- Enterprise Security and Governance MLOps (by Diego Oppenheimer)
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
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