What happens when reviewers receive AI feedback in their reviews?

TL;DR

This study empirically examines the impact of an AI feedback tool on peer review at ICLR 2025, revealing both benefits and challenges of AI augmentation in high-stakes academic review processes.

Contribution

First empirical analysis of an AI feedback tool in live peer review, providing insights into reviewer engagement, perceptions, and implications for future AI-assisted review systems.

Findings

Reviewers found AI feedback useful but raised concerns about fairness.

AI assistance improved review quality but threatened reviewer autonomy.

Design recommendations to balance AI benefits with human oversight.

Abstract

AI is reshaping academic research, yet its role in peer review remains polarising and contentious. Advocates see its potential to reduce reviewer burden and improve quality, while critics warn of risks to fairness, accountability, and trust. At ICLR 2025, an official AI feedback tool was deployed to provide reviewers with post-review suggestions. We studied this deployment through surveys and interviews, investigating how reviewers engaged with the tool and perceived its usability and impact. Our findings surface both opportunities and tensions when AI augments in peer review. This work contributes the first empirical evidence of such an AI tool in a live review process, documenting how reviewers respond to AI-generated feedback in a high-stakes review context. We further offer design implications for AI-assisted reviewing that aim to enhance quality while safeguarding human expertise, agency, and responsibility.