AI Explainability 360 Open Source Toolkit

This extensible open source toolkit can help you comprehend how machine learning models predict labels by various means throughout the AI application lifecycle. Containing eight state-of-the-art algorithms for interpretable machine learning as well as metrics for explainability, it is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education. We invite you to use it and improve it.


Although it is ultimately the consumer who determines the quality of an explanation, the research community has proposed quantitative metrics as proxies for explainability.