Responsible AI, bias detection, fairness, safety, alignment, and the societal impact of artificial intelligence.
- Fast.ai: Practical Data Ethics - Hands-on course on ethical implications of data science and AI.
Beginner - MIT: Ethics of AI - Exploring ethical considerations in AI development and deployment.
Beginner - Elements of AI: Ethics Module - AI ethics within a comprehensive, beginner-friendly AI course.
Beginner - Coursera: AI Ethics in Practice - Free to audit course on applying ethical principles to AI.
Beginner - Harvard: Ethics and Governance of AI - Harvard's perspective on AI governance and ethics.
Intermediate
- Fairness and Machine Learning (Barocas, Hardt, Narayanan) - Free textbook on fairness, bias, and accountability in ML.
Intermediate - AI Ethics Brief (Montreal AI Ethics Institute) - Weekly digests and research on AI ethics.
All Levels - Anthropic: AI Safety Research - Free research papers on AI alignment and safety.
Advanced - The Alignment Problem (Resources) - Resources from the book on AI alignment challenges.
Beginner
- Google: Responsible AI Practices - Guide on building AI responsibly from Google.
All Levels - Stanford HAI: AI Policy Resources - Research and resources on AI ethics and policy.
Intermediate - Partnership on AI - Resources, case studies, and guidelines for responsible AI.
All Levels - AI Fairness 360 (IBM) - Open-source toolkit to detect and mitigate bias in ML models.
Intermediate - Model Cards for Model Reporting - Framework for transparent model documentation.
Beginner