AI Explainability 360: An Open-Source Toolkit of Explanation Techniques, its Impact, and Recent Directions

Oregon Convention Center 

This two-part talk will cover two different meanings of "360" as it relates to explainable AI.

The first part will present AI Explainability 360, an open-source software toolkit that we created featuring ten diverse explanation methods and two evaluation metrics. This diversity was aimed at addressing the needs of multiple stakeholders touched by AI and machine learning algorithms, whether they be affected citizens, domain experts, system developers, or government regulators. The impact of the toolkit will be discussed through several case studies, statistics, and community feedback, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation.

The second part of the talk will offer a selective survey of our recent research directions in explainable AI. In particular, we will discuss work on making perturbation-based explanation methods (such as LIME and SHAP) more reliable and efficient. These ideas may be of interest to statisticians looking to contribute to the area.