# Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

arXiv: https://arxiv.org/abs/2006.16437 · HTML: https://mlsanity.com/paper/2006.16437/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 30 of 38)

- [ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing](https://mlsanity.com/paper/2306.00622/) (2023)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach](https://mlsanity.com/paper/2211.06398/) (2022)
- [Detecting LLM-Generated Peer Reviews](https://mlsanity.com/paper/2503.15772/) (2025)
- [You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism](https://mlsanity.com/paper/2110.14802/) (2021)
- [Matching Papers and Reviewers at Large Conferences](https://mlsanity.com/paper/2202.12273/) (2022)
- [Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment](https://mlsanity.com/paper/2411.03417/) (2024)
- [Making Paper Reviewing Robust to Bid Manipulation Attacks](https://mlsanity.com/paper/2102.06020/) (2021)
- [A Gold Standard Dataset for the Reviewer Assignment Problem](https://mlsanity.com/paper/2303.16750/) (2023)
- [Chain-of-Factors Paper-Reviewer Matching](https://mlsanity.com/paper/2310.14483/) (2023)
- [A Dataset on Malicious Paper Bidding in Peer Review](https://mlsanity.com/paper/2207.02303/) (2022)
- [RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation](https://mlsanity.com/paper/2407.20684/) (2024)
- [The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review](https://mlsanity.com/paper/2408.13430/) (2024)
- [No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning](https://mlsanity.com/paper/2303.14443/) (2023)
- [On the Detection of Reviewer-Author Collusion Rings From Paper Bidding](https://mlsanity.com/paper/2402.07860/) (2024)
- [Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions](https://mlsanity.com/paper/2412.06606/) (2024)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)
- [Combating Collusion Rings is Hard but Possible](https://mlsanity.com/paper/2112.08444/) (2021)
- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?](https://mlsanity.com/paper/2211.12966/) (2022)
- [Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review](https://mlsanity.com/paper/2304.11160/) (2023)
- [Some Ethical Issues in the Review Process of Machine Learning Conferences](https://mlsanity.com/paper/2106.00810/) (2021)
- [Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment](https://mlsanity.com/paper/2207.11315/) (2022)
- [Reviewer assignment problem: A scoping review](https://mlsanity.com/paper/2305.07887/) (2023)
- [Identity Theft in AI Conference Peer Review](https://mlsanity.com/paper/2508.04024/) (2025)
- [Auctions and Peer Prediction for Academic Peer Review](https://mlsanity.com/paper/2109.00923/) (2021)
- [Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty](https://mlsanity.com/paper/2411.02654/) (2024)
- [Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing](https://mlsanity.com/paper/2411.11437/) (2024)
- [Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism](https://mlsanity.com/paper/2601.15249/) (2026)

## Abstract (arXiv)

We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as part of quid-pro-quo arrangements with the authors; (ii) "torpedo reviewing," where reviewers deliberately attempt to get assigned to certain papers that they dislike in order to reject them; (iii) reviewer de-anonymization on release of the similarities and the reviewer-assignment code. On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair. We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case. Finally, we experimentally evaluate our algorithms on datasets from past conferences, where we observe that they can limit the chance that any malicious reviewer gets assigned to their desired paper to 50% while producing assignments with over 90% of the total optimal similarity. Our algorithms still achieve this similarity while also preventing reviewers with close associations from being assigned to the same paper.