{
  "id": 1314,
  "url": "https://arxiv.org/abs/2606.08376v1",
  "title": "RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations",
  "summary": "As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity. Although existing governance frameworks articulate high-level principles for responsible AI, large-scale empirical resources for tracking and analyzing real-world AI risk incidents remain limited. Existing incident collections are often manually curated, relatively small in scale, and insufficient for continuous, ",
  "authors": "Leihan Zhang, Wecheng Ye, Xianlong Ma, Haochuan Liu, Yang Li, Qianyu Zhang et al.",
  "category": "research",
  "topics": "regulation,safety-alignment,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-07T00:06:27.000Z",
  "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/1314",
  "original_url": "https://arxiv.org/abs/2606.08376v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}