AI RESEARCH
Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding
arXiv CS.AI
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ArXi:2603.25251v1 Announce Type: cross Explainable AI (XAI) methods are commonly evaluated with functional metrics such as correctness, which computationally estimate how accurately an explanation reflects the model's reasoning. Higher correctness is assumed to produce better human understanding, but this link has not been tested experimentally with controlled levels.