# Identity Theft in AI Conference Peer Review

arXiv: https://arxiv.org/abs/2508.04024 · HTML: https://mlsanity.com/paper/2508.04024/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions](https://mlsanity.com/paper/2412.06606/) (2024)
- [A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions](https://mlsanity.com/paper/2403.01015/) (2024)

## Built on by (most cited first, 3 of 3)

- [Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents](https://mlsanity.com/paper/2605.09915/) (2026)
- [Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition](https://mlsanity.com/paper/2510.12692/) (2025)
- [Smooth Partial Lotteries for Stable Randomized Selection](https://mlsanity.com/paper/2605.20069/) (2026)

## 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.