A curated hub with essential XAI research papers, tools, and tutorials, fostering innovation and understanding in Explainable AI.
MIT
awesome-XAI: A Curated Collection of XAI Resources
Introduction
Welcome to awesome-XAI, a comprehensive repository dedicated to the dynamic and evolving field of Explainable Artificial Intelligence (XAI). This project is a curated list of influential papers, cutting-edge libraries, and valuable resources aimed at researchers, practitioners, and enthusiasts in XAI.
What is XAI?
Explainable Artificial Intelligence (XAI) is a branch of AI focused on making the decision-making processes of AI systems transparent and understandable to humans. This field addresses the growing need for accountability and comprehensibility in AI systems, especially in critical applications like healthcare, finance, and autonomous vehicles.
Features
Research Papers: A selection of seminal and recent papers in XAI, categorized by themes such as interpretability methods, case studies, ethical implications, and more.
Libraries and Tools: Links to open-source libraries and tools that are useful for implementing XAI methods in various programming languages.
Tutorials and Guides: Step-by-step tutorials and comprehensive guides for beginners and advanced users alike.
Community Contributions: A section for community-contributed resources, discussions, and collaborations.
A tree-based writing interface for GPT-3, visualizing the probability mass distribution of the future multiverse predicted by a language model conditioned on a prompt.
License
This project is licensed under the MIT License. For more details, see the LICENSE file in the repository.
Acknowledgments
Special thanks to the contributors, researchers, and practitioners who have made this project possible. Your dedication to advancing the field of XAI is deeply appreciated.