{
  "id": 17090,
  "url": "https://arxiv.org/abs/2608.05602v1",
  "title": "Epistemic Trustworthiness in Generative AI: A Normative Framework for Warranted Reliance in High-Stakes Workflows",
  "summary": "Generative AI systems are increasingly deployed in high-stakes professional contexts, where their outputs shape what users believe, how they reason, and what they treat as settled. This raises a central question for responsible AI: under what conditions is reliance on generative AI outputs epistemically warranted rather than behaviourally induced? Existing frameworks largely ask whether AI outputs are accurate, fair, explainable, safe, or trusted by users. These questions remain necessary, and e",
  "authors": "Nimisha Karnatak, Max Van Kleek, Nigel Shadbolt",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T04:53:46.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17090",
  "original_url": "https://arxiv.org/abs/2608.05602v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}