Explainable AI does not provide the explanations end-users are asking for

George \v{C}evora; Savio Rozario

Explainable AI does not provide the explanations end-users are asking for

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arxiv 2302.11577 v2 pith:U46LV5BU submitted 2023-01-25 cs.HC cs.AIcs.LG

Savio Rozario , George Čevora This is my paper · ORCID

classification cs.HCcs.AIcs.LG

keywords systemstrustexplainablegainingadoptionalongsideartificialasking

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Explainable Artificial Intelligence (XAI) techniques are frequently required by users in many AI systems with the goal of understanding complex models, their associated predictions, and gaining trust. While suitable for some specific tasks during development, their adoption by organisations to enhance trust in machine learning systems has unintended consequences. In this paper we discuss XAI's limitations in deployment and conclude that transparency alongside with rigorous validation are better suited to gaining trust in AI systems.

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Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
cs.LG 2025-06 conditional novelty 5.0 of 10

An in-context LLM framework that produces dual expert and non-expert explanations, evaluated on well-being clustering with a user study and LIME-alignment metrics.

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