Awesome Explainable AI
A curated collection of research papers, books, courses, and Python libraries for explainable AI (XAI) and machine learning interpretability.
What is Awesome Explainable AI?
Awesome-explainable-AI is a curated GitHub repository that serves as a comprehensive collection of research materials, tools, and resources focused on explainable artificial intelligence (XAI) and machine learning interpretability. It addresses the challenge of navigating the rapidly growing body of work on making AI models transparent and understandable by organizing papers, libraries, books, and courses into a structured taxonomy.
AI/ML researchers, data scientists, and practitioners who need to understand, implement, or evaluate explainability methods for machine learning models, particularly those working on model transparency, ethics, or deployment in sensitive domains.
It provides a centralized, well-organized hub that saves time searching for XAI resources, offers a clear taxonomy to understand different interpretability approaches, and includes practical tools like Python libraries alongside academic research.
Overview
A collection of research materials on explainable AI/ML
Use Cases
Best For
- Researchers conducting literature reviews on explainable AI methods
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