{
  "id": 4141,
  "url": "https://arxiv.org/abs/2605.18036v1",
  "title": "Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users",
  "summary": "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",
  "authors": "Alfio Ventura, Tim Katzke, Jan Corazza, Mustafa Yalçıner",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": "uk",
  "published_at": "2026-05-18T08:26:30.000Z",
  "fetched_at": "2026-07-14T16:30:45.940Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4141",
  "original_url": "https://arxiv.org/abs/2605.18036v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}