mlsanityData to 7 Oct 2026

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

arXiv 2302.11577 ↗First posted 25 Jan 2023cs.HC cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 25 Jan 2023.

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16 of 20 references.

References · 16

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4 more
  1. 2022The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspectivecited by 156
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  1. 2025Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AIcited by 0