andy6804tw/crazyai-xai
全民瘋AI系列 [探索可解釋人工智慧]
This project helps data scientists, AI researchers, and machine learning engineers understand why their AI models make certain predictions. It provides tutorials and code examples for various Explainable AI (XAI) techniques, allowing users to input complex machine learning models and interpret their decision-making processes, enhancing trust and transparency. The output includes explanations of how different features influence model outcomes across various data types like images, text, and tabular data.
Use this if you need to demystify 'black box' AI models, ensure compliance, or build user trust by understanding and explaining how your models arrive at their conclusions.
Not ideal if you are looking for a plug-and-play XAI library or a tool for non-technical users, as it focuses on in-depth understanding and practical implementation for technical practitioners.
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Jupyter Notebook
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Last pushed
Nov 23, 2025
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