A System's Approach Taxonomy for User-Centred XAI: A Survey
This work provides a foundational taxonomy to improve XAI usability for diverse users, though it is incremental as it builds on existing survey efforts.
The paper addresses the lack of a user-centered framework in eXplainable AI (XAI) by proposing a unified taxonomy based on General System's Theory to evaluate the suitability of explanations for all user types, including developers and end users.
Recent advancements in AI have coincided with ever-increasing efforts in the research community to investigate, classify and evaluate various methods aimed at making AI models explainable. However, most of existing attempts present a method-centric view of eXplainable AI (XAI) which is typically meaningful only for domain experts. There is an apparent lack of a robust qualitative and quantitative performance framework that evaluates the suitability of explanations for different types of users. We survey relevant efforts, and then, propose a unified, inclusive and user-centred taxonomy for XAI based on the principles of General System's Theory, which serves us as a basis for evaluating the appropriateness of XAI approaches for all user types, including both developers and end users.