Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding
Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, sometimes termed 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. Correctness was defined against a known ground truth, not estimated from a trained model. Participants predicted a simulated AI's...
| Authors | Gregor Baer, Chao Zhang, Isel Grau et al. |
|---|---|
| Primary category | cs.HC |
| Published | 26 March 2026 |
| Last updated at source | 11 August 2026 |
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