{
  "id": 5439,
  "url": "https://arxiv.org/abs/2604.21854v1",
  "title": "Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation",
  "summary": "Artificial intelligence now decides who receives a loan, who is flagged for criminal investigation, and whether an autonomous vehicle brakes in time. Governments have responded: the EU AI Act, the NIST Risk Management Framework, and the Council of Europe Convention all demand that high-risk systems demonstrate safety before deployment. Yet beneath this regulatory consensus lies a critical vacuum: none specifies what ``acceptable risk'' means in quantitative terms, and none provides a technical m",
  "authors": "Natan Levy, Gadi Perl",
  "category": "research",
  "topics": "regulation,finance-investment",
  "orgs": null,
  "regions": "eu",
  "published_at": "2026-04-23T16:50:35.000Z",
  "fetched_at": "2026-07-14T16:31:44.626Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5439",
  "original_url": "https://arxiv.org/abs/2604.21854v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}