jphall663/awesome-machine-learning-interpretability

Organizes resources across interpretability, fairness, governance, and safety—spanning technical tools (Python/R/JavaScript packages, benchmarks, datasets), policy frameworks, incident documentation, and critical perspectives on AI's environmental and social costs. The collection bridges academic research, regulatory guidance, and real-world AI failure case studies, enabling practitioners to implement responsible ML across the full development lifecycle. Actively maintained with structured categories for education, auditing checklists, red-teaming resources, and emerging topics like generative AI explainability and agentic AI governance.

3,996 stars. Actively maintained with 10 commits in the last 30 days.