XAI
A curated collection of papers, methods, critiques, and resources for Explainable AI (XAI) and Interpretable Machine Learning.
What is XAI?
Awesome XAI is a curated GitHub repository that aggregates papers, methods, tools, and resources related to Explainable AI (XAI) and Interpretable Machine Learning. It aims to help researchers and practitioners understand, evaluate, and implement techniques for making AI and ML models more transparent and interpretable. The collection spans landmark research, practical toolkits, critical debates, and community insights.
Machine learning researchers, data scientists, AI practitioners, and students who need to understand, apply, or critique explainability methods in their work. It's particularly valuable for those building or auditing transparent AI systems.
It provides a single, well-organized entry point to the expansive XAI literature and tooling, saving time on literature review and offering balanced perspectives through included critiques and debates.
Overview
Awesome Explainable AI (XAI) and Interpretable ML Papers and Resources
Use Cases
Best For
- Researchers conducting literature reviews on model interpretability methods
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