{
  "id": 4462,
  "url": "https://arxiv.org/abs/2605.12120v1",
  "title": "To Whom Do Language Models Align? Measuring Principal Hierarchies Under High-Stakes Competing Demands",
  "summary": "Language models deployed in high-stakes professional settings face conflicting demands from users, institutional authorities, and professional norms. How models act when these demands conflict reveals a principal hierarchy -- an implicit ordering over competing stakeholders that determines, for instance, whether a medical AI receiving a cost-reduction directive from a hospital administrator complies at the expense of evidence-based care, or refuses because professional standards require it. Acro",
  "authors": "Fangyi Yu, Nabeel Seedat, Jonathan Richard Schwarz, Andrew M. Bean",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-12T13:36:39.000Z",
  "fetched_at": "2026-07-14T16:30:59.239Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4462",
  "original_url": "https://arxiv.org/abs/2605.12120v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}