Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards

Jaeho Kim, Yunseok Lee, Seulki Lee

International Conference on Machine Learning 2025 · Oral

In this insightful and timely talk at ICML 2025, Jaeho Kim, along with Yunseok Lee and Seulki Lee from UNIST South Korea, presented a compelling position paper addressing the escalating crisis in AI conference peer review. Titled "The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards," the presentation critically dissects the systemic failures that increasingly render paper acceptance dependent on a "reviewer lottery" rather than the intrinsic quality of research. The core argument posits that without immediate and significant structural reforms, the integrity and fairness of top-tier AI conferences are at risk, particularly impacting junior researchers.

AI review

A position paper diagnosing real dysfunction in ML conference peer review and proposing two structural remedies — bidirectional author feedback and a reviewer reward system. The diagnosis is widely shared and the motivation is genuine, but the proposed solutions are underspecified, the supporting evidence is thin to nonexistent, and the core mechanisms introduce new failure modes that receive only cursory treatment. This is advocacy dressed as analysis.