Awesome-ML-Model-Governance
Model Governance, Ethics, Responsible AI
https://github.com/nholuongut/Awesome-ML-Model-Governance
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Model Governance, Ethics, Responsible AI
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- Secure, privacy-preserving and federated machine learning in medical imaging
- Philosophical grounding of AI fairness in Business Ethics
- ABOUT ML - Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles.
- 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
- 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
- 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
- Book: "Fairness and machine learning: Limitations and Opportunities." Barocas, S., Hardt, M. and Narayanan, A., 2018.
- ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
- What are model governance and model operations? – O’Reilly
- 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.
- Philosophical grounding of AI fairness in Business Ethics
- The Open Ethics Canvas by the Open Ethics
- 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)
- 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
- 😈 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
- 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
- The Open Ethics Canvas by the Open Ethics
- 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)
- Navigate the road to Responsible AI – Gradient Flow Blog
- 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.)
- Biases in AI Systems. A survey for practitioners
- 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)
- 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)
- 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
- 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
- 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
- Adversarial Machine Learning 101
- ATLAS - Adversarial Threat Landscape for Artificial-Intelligence Systems
- 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
- 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.
- 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
- Book: "The Framework for ML Governance" by Kyle Gallatin. 2021. O'Reilly Media
- 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
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