Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
Trust calibration -- aligning user trust judgment with model capability -- is crucial for safe deployment of explainable AI (XAI), yet is often evaluated via global trust ratings detached from objective performance evidence. We present a preregistered, incentivized between-subject online study (N=418 representative UK sample) on explainable skin-lesion classification that disentangles expectation-setting from experienced performance. Participants completed 15 case evaluations using a fixed XAI p
Record details
Published: 18 May 2026
Source: arXiv
Category: Research
Topics: Transparency
Retrieved: 14 July 2026
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ethics.ai (18 May 2026), “Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users,” evidence record 4141, https://ethics.ai/record/4141 (originally published by arXiv).
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