{
  "id": 1321,
  "url": "https://arxiv.org/abs/2606.08172v1",
  "title": "The Governance of Human-LLM Interaction: Safety Gating, Civility Steering, and Affective Default Lock-In",
  "summary": "Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited control over how these systems communicate. We frame interaction style as a governance object: provider-side alignment not only blocks harmful content, but also stabilizes communicative defaults that shape users' epistemic distance, relational expectations, and capacity to opt out of emotionalized or anthropomorphic interaction. We introduce a determi",
  "authors": "Manuele Reani, Hongjian Zhang, Hongyu Tian",
  "category": "research",
  "topics": "regulation,safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-06T13:36:37.000Z",
  "fetched_at": "2026-07-14T14:15:12.455Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1321",
  "original_url": "https://arxiv.org/abs/2606.08172v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}