{
  "id": 863,
  "url": "https://arxiv.org/abs/2606.30661v1",
  "title": "Understanding Censorship in Large Language Models: From Mechanisms to Governance",
  "summary": "Large language models (LLMs) increasingly mediate access to information, yet their responses are shaped by training-data curation, alignment procedures, provider policies, inference-time moderation, and jurisdictional regulation. This paper examines LLM censorship as a sociotechnical phenomenon that extends beyond explicit refusals to include omissions, selective emphasis, framing effects, and geographically variable content controls. We synthesize recent empirical studies, provider case studies",
  "authors": "Quanyan Zhu",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-16T18:48:20.000Z",
  "fetched_at": "2026-07-14T14:14:50.325Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/863",
  "original_url": "https://arxiv.org/abs/2606.30661v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}