{
  "id": 11623,
  "url": "https://arxiv.org/abs/2607.14152v1",
  "title": "\"Trust Junk\" Leads to Unjustified Support for Highly Discriminatory Predictive Models",
  "summary": "The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models. In this paper, we use a crowdsourced study to show that providing accurate (but superfluous or irrelevant) data in a model explanation can, in fact, result in unjustified trust and other positive beliefs about a model, even when the model is patently discriminatory and unfair. Our results suggest that XAI designers and developers n",
  "authors": "Michael Correll, Lucy Havens, Mahsan Nourani",
  "category": "research",
  "topics": "bias-fairness,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T17:48:35.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "arxiv-hci",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11623",
  "original_url": "https://arxiv.org/abs/2607.14152v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}