shapash and awesome-machine-learning-interpretability

About shapash

MAIF/shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

About awesome-machine-learning-interpretability

jphall663/awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

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.