{
  "id": 6051,
  "url": "https://arxiv.org/abs/2605.16281v1",
  "title": "From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework",
  "summary": "Artificial intelligence systems are increasingly deployed in high-stakes domains, yet it remains unclear whether existing governance frameworks ensure accountability after deployment. This study makes two contributions. First, it presents a cross-regulatory empirical analysis of 480 real-world AI incidents from the AI Incident Database (AIID), evaluating their alignment with post-deployment provisions in three major governance frameworks: the EU AI Act (Articles 72-73), the NIST AI Risk Manageme",
  "authors": "Ummara Mumtaz, Summaya Mumtaz",
  "category": "research",
  "topics": "regulation,safety-alignment,transparency",
  "orgs": null,
  "regions": "eu",
  "published_at": "2026-04-10T21:22:55.000Z",
  "fetched_at": "2026-07-14T16:32:11.185Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6051",
  "original_url": "https://arxiv.org/abs/2605.16281v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}