AI Explainability 360: Impact and Design

This section highlights the impact of the AIX360 toolkit in the first two years since its release. It describes several different forms of impact on real problem domains and the open source community. This impact has resulted in improvements in multiple metrics: accuracy, semiconductor yield, satisfaction rate, and domain expert time. The current version of the AIX360 toolkit includes ten explainability algorithms described in Table 1 covering different ways of explaining. Explanation methods could be either local or global, where the former refers to explaining an AI model's decision for a single instance, while the latter refers to explaining a model in its entirety.