The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Year: 2025
Type: article
Abstract: We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on perceived quality. In total, we received 1,342 rankings, each from a different author, covering 2,592 submissions. In this paper, we present an empirical analysis of how author-provided rankings could be leveraged to improve peer review processes at machine learning conferences. We focus on the Isotonic Mechanism, which calibrates raw review scores using the author-provided rankings. Our analysis shows that these ranking-calibrated scores out-perform the raw review scores in estimating the ground truth “expected review scores” in terms of both squared and absolute error metrics. Furthermore, we propose several cautious, low-risk applications of the Isotonic Mechanism and author-provided rankings in peer review, including supporting senior area chairs in overseeing area chairs’ recommendations, assisting in the selection of paper awards, and guiding the recruitment of emergency reviewers.
DOI: 10.17615/zhk4-4y76
Authors: Cho, Kyunghyun, Su, Buxin, Fan, Jianqing, Li, Didong, Collina, Natalie, Yan, Yuling, Su, Weijie, Zhang, Jiayao, Roth, Aaron
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4416200444
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