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Why these Explanations? Selecting Intelligibility Types for Explanation Goals

The increasing ubiquity of artificial intelligence (AI) has spurred the development of explainable AI (XAI) to make AI more understandable. Even as novel algorithms for explanation are being developed, researchers have called for more human interpretability. While empirical user studies can be conducted to evaluate explanation effectiveness, it remains unclear why specific explanations are helpful for understanding. We leverage a recently developed conceptual framework for user-centric reasoned XAI that draws from foundational concepts in philosophy, cognitive psychology, and AI to identify pathways for how user reasoning drives XAI needs. We identified targeted strategies for applying XAI facilities to improve understanding, trust and decision performance. We discuss how our framework can be extended and applied to other domains that need usercentric XAI. This position paper seeks to promote the design of XAI features based on human reasoning needs.