jphall663/awesome-machine-learning-interpretability
A curated list of resources on responsible machine learning, interpretability, explainability, and AI safety.
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This repository is a curated list (awesome-list) aggregating resources on responsible machine learning, model interpretability, and explainable AI (XAI). Topics covered include fairness, transparency, privacy-preserving ML, AI governance, and AI safety. The collection has since been reorganized and expanded into the HallResearch.ai Library organization, with this repository preserved as a legacy entry point.
Frequently asked
- What is jphall663/awesome-machine-learning-interpretability?
- A curated list of resources on responsible machine learning, interpretability, explainability, and AI safety.
- Is awesome-machine-learning-interpretability open source?
- Yes — jphall663/awesome-machine-learning-interpretability is open source, released under the CC0-1.0 license.
- How popular is awesome-machine-learning-interpretability?
- jphall663/awesome-machine-learning-interpretability has 4k stars on GitHub.
- Where can I find awesome-machine-learning-interpretability?
- jphall663/awesome-machine-learning-interpretability is on GitHub at https://github.com/jphall663/awesome-machine-learning-interpretability.