Avoiding a Tragedy of the Commons in the Peer Review Process
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Abstract (arXiv)
Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 1.
10 papers.
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewardscited by 34
- 2021You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanismcited by 18
- 2024The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Reviewcited by 7
- 2021Some Ethical Issues in the Review Process of Machine Learning Conferencescited by 4
- 2024Questionable practices in machine learningcited by 3
- 2025How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Reviewcited by 3
- 2021Auctions and Peer Prediction for Academic Peer Reviewcited by 2
- 2022Strengthening Subcommunities: Towards Sustainable Growth in AI Researchcited by 1
- 2023Eliciting Honest Information From Authors Using Sequential Reviewcited by 0
- 2026How Much Were You Told? Measuring External Information in Peer Reviewscited by 0
- 2026How Much Were You Told? Measuring External Information in Peer Reviews23 Sep 2026
- 2025How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review2 Oct 2025
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards8 May 2025
- 2024The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review24 Aug 2024
- 2024Questionable practices in machine learning17 Jul 2024
- 2023Eliciting Honest Information From Authors Using Sequential Review24 Nov 2023
- 2022Strengthening Subcommunities: Towards Sustainable Growth in AI Research18 Apr 2022
- 2021You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism27 Oct 2021
- 2021Auctions and Peer Prediction for Academic Peer Review27 Aug 2021
- 2021Some Ethical Issues in the Review Process of Machine Learning Conferences1 Jun 2021