{
  "id": 7627,
  "url": "https://arxiv.org/abs/2603.05801v1",
  "title": "Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks",
  "summary": "Large language models (LLMs) are increasingly used to make sense of ambiguous, open-textured, value-laden terms. Platforms routinely rely on LLMs for content moderation, asking them to label text based on disputed concepts like \"hate speech\" or \"incitement\"; hiring managers may use LLMs to rank who counts as \"qualified\"; and AI labs increasingly train models to self-regulate under constitutional-style ambiguous principles such as \"biased\" or \"legitimate\". This paper introduces ambiguity collapse",
  "authors": "Shira Gur-Arieh, Angelina Wang, Sina Fazelpour",
  "category": "research",
  "topics": "bias-fairness,regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-06T01:23:17.000Z",
  "fetched_at": "2026-07-14T16:33:21.050Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7627",
  "original_url": "https://arxiv.org/abs/2603.05801v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}