{
  "id": 401,
  "url": "https://arxiv.org/abs/2606.31567v1",
  "title": "FLARE-AI: Flaw Reporting for AI",
  "summary": "Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant stakeholders. As a result, good-faith reporters duplicate effort by submitting many different forms, and recipients lack standardized, triage-ready information. We audit 12 reporting systems published ",
  "authors": "Shayne Longpre, Elaine Zhu, Carson Ezell, Avijit Ghosh, Sean McGregor, Kevin Paeth et al.",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T12:27:09.000Z",
  "fetched_at": "2026-07-14T14:14:32.643Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/401",
  "original_url": "https://arxiv.org/abs/2606.31567v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}