Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process

TL;DR

This paper advocates for adopting a more transparent and regulated peer review process in AI and ML conferences to enhance community involvement and improve the quality of scientific evaluation.

Contribution

It provides an analysis of current peer review models and argues for a shift towards more open and regulated review systems in AI/ML research communities.

Findings

Open peer review increases transparency and community engagement.

Analysis of Paper Copilot shows high global researcher involvement.

Community favors transparent review processes for better scientific integrity.

Abstract

The rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review platforms. Some have fully embraced open peer reviews, allowing public visibility throughout the process, while others adopt hybrid approaches, such as releasing reviews only after final decisions or keeping reviews private despite using open peer review systems. In this work, we analyze the strengths and limitations of these models, highlighting the growing community interest in transparent peer review. To support this discussion, we examine insights from Paper Copilot, a website launched two years ago to aggregate and analyze AI / ML conference data while engaging a global audience. The site has attracted over 200,000 early-career researchers, particularly those aged 18-34 from 177 countries, many of whom are actively engaged in the peer review process. Drawing on our findings, this position paper advocates for a more transparent, open, and well-regulated peer review aiming to foster greater community involvement and propel advancements in the field.