AI Explainability 360: Impact and Design

We also introduced a taxonomy to The increasing use of artificial intelligence (AI) systems in navigate the space of explanation methods, not only the ten high stakes domains has been coupled with an increase in societal in the toolkit but also the broader literature on explainable demands for these systems to provide explanations for AI. The taxonomy was intended to be usable by consumers their outputs. This societal demand has already resulted in with varied backgrounds to choose an appropriate explanation new regulations requiring explanations (Goodman and Flaxman method for their application. AIX360 differs from other 2016; Wachter, Mittelstadt, and Floridi 2017; Selbst open source explainability toolkits (see Arya et al. (2020) and Powles 2017; Pasternak 2019). Explanations can allow for a list) in two main ways: 1) its support for a broad and users to gain insight into the system's decision-making process, diverse spectrum of explainability methods, implemented in which is a key component in calibrating appropriate a common architecture, and 2) its educational material as trust and confidence in AI systems (Doshi-Velez and Kim discussed below.