An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process

David Tran, Alexander V Valtchanov, Keshav R Ganapathy, Raymond Feng, Eric Victor Slud, Micah Goldblum, Tom Goldstein

Keywords: Conference Review, OpenReview, Gender, Bias, Reproducibility, Fairness

Abstract: Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias. In this work, we critically analyze the review process through a comprehensive study of papers submitted to ICLR between 2017 and 2020. We quantify reproducibility/randomness in review scores and acceptance decisions, and examine whether scores correlate with paper impact. Our findings suggest strong institutional bias in accept/reject decisions, even after controlling for paper quality. Furthermore, we find evidence for a gender gap, with female authors receiving lower scores, lower acceptance rates, and fewer citations per paper than their male counterparts. We conclude our work with recommendations for future conference organizers.

One-sentence Summary: We study the conference review process to quantify reproducibility and bias.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2010.05137/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=TC4vxpQJbl