awesome-machine-learning-interpretability

Why we included this project

This curated index collects pointers to tools, papers, and frameworks for making machine learning models explainable. The coverage also reaches into fairness, privacy-preserving techniques, and AI safety, so a data scientist or ML engineer who needs to justify a model's decisions, or meet a transparency requirement, can find the right resource without wading through scattered sources. The project has since moved into the HallResearch.ai Library, which now carries the actively maintained branches on AI governance and assessment; this repository stays up as the original archive. Since it is a reading list rather than a software package, its real value is the breadth and organization of the references, which helps newcomers build a mental map of the responsible ML field before they commit to specific tooling.