mlsanityData to 7 Oct 2026

Identity Theft in AI Conference Peer Review

arXiv 2508.04024 ↗First posted 6 Aug 2025cs.DL cs.AI cs.CRcited by 3

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Abstract (arXiv)

We discuss newly uncovered cases of identity theft in the scientific peer-review process within artificial intelligence (AI) research, with broader implications for other academic procedures. We detail how dishonest researchers exploit the peer-review system by creating fraudulent reviewer profiles to manipulate paper evaluations, leveraging weaknesses in reviewer recruitment workflows and identity verification processes. The findings highlight the critical need for stronger safeguards against identity theft in peer review and academia at large, and to this end, we also propose mitigating strategies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 6 Aug 2025.

Builds on

3 of 9 references.

References · 3

  1. 2020Mitigating Manipulation in Peer Review via Randomized Reviewer Assignmentscited by 38
  2. 2024Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusionscited by 7
  3. 2024A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussionscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.