[2603.25251] Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding

Computer Science > Human-Computer Interaction arXiv:2603.25251 (cs) [Submitted on 26 Mar 2026] Title:Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding Authors:Gregor Baer, Chao Zhang, Isel Grau, Pieter Van Gorp View a PDF of the paper titled Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding, by Gregor Baer and 3 other authors View PDF HTML (experimental) Abstract: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. We conducted a user study (N=200) that manipulated explanation correctness at four levels (100%, 85%, 70%, 55%) in a time series classification task where participants could not rely on domain knowledge or visual intuition and instead predicted the AI's decisions based on explanations (forward simulation). Correctness affected understanding, but not at every level: performance dropped at 70% and 55% correctness relative to fully correct explanations, while further degradation below 70% produced no additional loss. Rather than shifting performance uniformly, lower correctness decreased the proportion of participants who learned the decision pattern. At the same time, even fully correct explanation...