{
  "id": 5962,
  "url": "https://arxiv.org/abs/2604.11065v1",
  "title": "AI Integrity: A New Paradigm for Verifiable AI Governance",
  "summary": "AI systems increasingly shape high-stakes decisions in healthcare, law, defense, and education, yet existing governance paradigms -- AI Ethics, AI Safety, and AI Alignment -- share a common limitation: they evaluate outcomes rather than verifying the reasoning process itself. This paper introduces AI Integrity, a concept defined as a state in which the Authority Stack of an AI system -- its layered hierarchy of values, epistemological standards, source preferences, and data selection criteria --",
  "authors": "Seulki Lee",
  "category": "research",
  "topics": "regulation,safety-alignment,healthcare,military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-13T06:45:30.000Z",
  "fetched_at": "2026-07-14T16:32:06.471Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5962",
  "original_url": "https://arxiv.org/abs/2604.11065v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}