{
  "id": 18724,
  "url": "https://arxiv.org/abs/2608.11256",
  "title": "Why AI Detection Fails for Academic Integrity",
  "summary": "arXiv:2608.11256v1 Announce Type: cross Abstract: Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled study of published English abstracts (four domains; 2013 to 2015 vs. 2023 to 2025), we quantify this policy failure under proxy human/AI labels at tau=0.50. Light \"refine abstract only\" edits, a proxy for guideline-compliant AI assistance, are flagged at 64 to 80% (Pang",
  "authors": "Jonathan A. Karr Jr, Grigorii Khvatskii, Ting Hua, Nitesh V. Chawla",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T04:00:00.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18724",
  "original_url": "https://arxiv.org/abs/2608.11256",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}