AwesomeResponsibleAI
A curated list of awesome academic research, books, code of ethics, courses, databases, data sets, frameworks, institutes, maturity models, newsletters, principles, podcasts, regulations, reports, responsible scale policies, tools and standards related to Responsible, Trustworthy, and Human-Centered AI.
https://github.com/AthenaCore/AwesomeResponsibleAI
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Academic Research
Adversarial ML
Artificial General Intelligence (AGI)
Artificial Intelligence Governance (AI Governance)
Bias
Challenges
Collections
- https://research.google/pubs/?collection=responsible-ai
- http://fairpipe.dssg.io
- http://fairpipe.dssg.io
- http://fairpipe.dssg.io
Drift
Ethical Data Products
-
Building Inclusive Products Through A/B Testing
- Jacques et al, 2020](https://arxiv.org/pdf/2002.05819))
LinkedIn - Article
- Article
- Article
- Article
- Article
Evaluation (of model explanations)
Explainability
- Article
- Dhurandhar et al., 2018
- Article
- Article
- Lundberg, et al. 2017
- Dash et al., 2018
- Article
- Dhurandhar et al., 2018
- Alvarez-Melis et al., 2018
- Article
- Article
- Article
- Article
- Article
- Article
- A Living and Curated Collection of Explainable AI Methods
Explainability/Interpretability/Mechanical Interpretability
Fairness
- LiFT: A Scalable Framework for Measuring Fairness in ML Applications
- Article
- Article
- Article
- Article
- Article
- Article
- Article
- Article
- Article and Materials
Regulation
Representation Engineering
Reproducible/Non-Reproducible Research
Risk
Sustainability
- Article
- Article
- Article
- Parcollet, T., & Ravanelli, M. 2021
- van Wynsberghe, A. 2021 - 6
- Lannelongue, L. et al. 2020
- van Wynsberghe, A. 2021 - 6
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Systems Risks
Books
Commercial / Propietary / Closed Access
- Varshney, K., 2022
- Thampi, A., 2022
- Mahoney, T., Varshney, K.R., Hind, M., 2020
- Rothman, D., 2020
- Kilroy, K., 2021
- Hall, P., Gill, N., Cox, B., 2020
- Privacy-Preserving Machine Learning
- Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI
- Interpretable Machine Learning With Python: Learn to Build Interpretable High-Performance Models With Hands-On Real-World Examples
- Book
- Book
- Book
- Nielsen, A., 2021
- Hall, P., Chowdhury, R., 2023
- Mahoney, T., Varshney, K.R., Hind, M., 2020
- Kilroy, K., 2021
- Hall, P., Gill, N., Cox, B., 2020
- Rothman, D., 2020
- Interpretable Machine Learning With Python: Learn to Build Interpretable High-Performance Models With Hands-On Real-World Examples
Open Access
Code of Ethics
Commercial / Propietary / Closed Access
Courses
Causality
Commercial / Propietary / Closed Access
- AGI Strategy
- AI Alignment
- AI Ethics - project.eu) project, an EU-backed initiative created to help professionals build ethical AI that’s fair, transparent, and accountable — not just technically accurate. | AI Ethics, Fairness |
- AI Ethics & Governance (AEG)
- AI Governance
- AI Governance
- AI Policy Clinic
- AI Safety, Ethics and Society - scale risks, and how society can manage and mitigate these risks. | AI Safety, AI Ethics, AI Governance |
- AI Security and Governance
- CS 2881 AI Safety
- CS 294-131: Trustworthy Deep Learning
- CS 860 - Algorithms for Private Data Analysis
- Data Justice (DJ) - oriented approach to examining the range of social, political, and material concerns arising within our increasingly datafied society | Ethics, Data Justice |
- Future of AI
- Introduction to Responsible Machine Learning - and-bolts course about increasing transparency, fairness, robustness, and security in machine learning. | Responsible AI |
- LLM evaluation
- Machine Learning Explainability - understandable insights from any model. | Explainability, Interpretability |
- Machine Learning in Production (17-445/17-645/17-745) / AI Engineering (11-695) - learned models. | MLOps, Responsible AI |