This article is the rejoinder to ``The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review,'' to appear in the Journal of the American Statistical Association with discussion. To address the practical and theoretical points raised by the discussants, we organize our response around four core themes: (i) formulating peer review as a statistical estimation problem; (ii) mitigating equity and strategic concerns in the deployment of the Isotonic Mechanism; (iii) incorporating complementary signals such as reviewer rankings and structured metadata; and (iv) exploring a human-centered framework for peer review in the era of generative AI.

翻译：本文是对即将发表在《美国统计协会杂志》（含讨论）上的《ICML 2023排名实验：审视ML/AI同行评审中的作者自我评估》一文的回复。针对讨论者提出的实践与理论问题，我们围绕四个核心主题组织回应：（i）将同行评审形式化为统计估计问题；（ii）在保序机制部署中缓解公平性与策略性关切；（iii）整合审稿人排名与结构化元数据等补充信号；（iv）探索生成式AI时代以人为中心的同行评审框架。