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Abstract Explainability is central to trust and accountability in artificial intelligence (AI) applications. The field of human‐centered explainable AI (HCXAI) arose as a response to mainstream explainable AI (XAI) which was focused on algorithmic perspectives and technical challenges, and less on the needs and contexts of the non‐expert, lay user. HCXAI is characterized by putting humans at the center of AI explainability. Taking a sociotechnical perspective, HCXAI prioritizes user and situational contexts, preferences reflection over acquiescence, and promotes the actionability of explanations. This review identifies the foundational ideas of HCXAI, how those concepts are operationalized in system design, how legislation and regulations might normalize its objectives, and the challenges that HCXAI must address as it matures as a field.
Michael Ridley (Sun,) studied this question.