arXiv cs.AI· Gregor Baer, Chao Zhang, Isel Grau, Pieter Van Gorp·· 11 小时前AI 评分31
解释正确性是否影响人类理解？XAI 计算评估与人类理解的关联研究
Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding
AI 导读
一项 N=200 的用户研究发现，XAI 解释的功能正确性并非在所有水平上都转化为更好的人类理解：在合成时间序列分类任务中，正确性降至 70% 和 55% 时参与者前向模拟准确率较 100% 下降，但 85% 与 100% 之间无显著差异，且低于 70% 后不再进一步下降。正确性下降主要减少准确预测的参与者人数而非均匀降低准确率，即使解释完全正确也只有部分参与者显著高于随机水平。
正文 · 原文
Abstract:Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, also called faithfulness or fidelity, which estimate how closely an explanation reflects the model's reasoning. Higher correctness is assumed to produce better human understanding, but this link has not been tested with controlled levels. We conducted a user study (N=200) that manipulated explanation correctness at four levels (100%, 85%, 70%, 55%) in a synthetic time series classification task where participants could not rely on domain knowledge or visual intuition. Explanations were constructed to resemble feature attributions, with correctness defined against a known ground truth rather than estimated from a trained model. Participants predicted a simulated AI's decisions from these explanations (forward simulation), and we used their forward simulation accuracy as a proxy for understanding. Correctness affected forward simulation accuracy, but not at every level: accuracy dropped at 70% and 55% relative to 100%, with no further reduction below 70% and no conclusive difference between 85% and 100%. Lower correctness reduced how many participants predicted accurately rather than lowering accuracy uniformly, and even with fully correct explanations, only a subset of participants predicted well above chance. In exploratory analyses, some participants predicted decisions accurately but described the wrong pattern when asked what the AI used, and self-reported ratings correlated with forward simulation accuracy only when explanations were fully correct. These findings suggest that, for feature-attribution-style explanations, not all differences in functional correctness translate to differences in human understanding, highlighting the need to validate functional metrics against human outcomes.
| Comments: | 33 pages, 11 figures, 3 tables |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.25251 [cs.HC] |
| (or arXiv:2603.25251v3 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2603.25251 arXiv-issued DOI via DataCite |
Submission history
From: Gregor Baer [view email]
[v1]
Thu, 26 Mar 2026 09:55:10 UTC (401 KB)
[v2]
Tue, 11 Aug 2026 11:29:30 UTC (451 KB)
[v3]
Wed, 4 Nov 2026 09:05:09 UTC (452 KB)
来源：arXiv cs.AI · arxiv.org