生成式人工智能在出版中的应用：编辑关于伦理与政策的专题讨论

中文摘要 AI 辅助

生成式人工智能（GenAI）正在重塑学术研究，其速度远超期刊为其使用制定稳定规范的速度。本文呈现了2026年联合统计会议（Joint Statistical Meetings）一个专题讨论的编辑性主题记录，该讨论汇集了来自数理统计、数据科学、生物医学统计和一般统计学术领域的编辑视角。讨论考察了期刊政策、披露、作者身份与研究诚信、同行评审保密性、研究人员培训、编辑工作量、获取途径以及学术出版可能的未来模式。小组成员共同承诺坚持人类问责制、保护机密投稿以及披露具有重大影响的辅助，同时在研究思路与证明的辅助、披露要求、自动评审和政策执行等方面提出了不同建议。通过区分共同原则与未解决的实施问题，本文阐明了统计出版面临的选择，并概述了一个在GenAI发展过程中评估政策和实践的议程。该记录旨在促进关于GenAI在科学传播中作用的持续讨论，并鼓励统计组织制定更健全的操作标准。

英文摘要

Generative artificial intelligence (GenAI) is reshaping scholarly research faster than journals have developed stable norms for its use. This article presents an edited thematic account of a 2026 Joint Statistical Meetings panel that brought together editorial perspectives from mathematical statistics, data science, biomedical statistics, and general statistical scholarship. The discussion examines journal policies, disclosure, authorship and research integrity, peer-review confidentiality, researcher training, editorial workload, access, and possible future models of scholarly publishing. Panelists shared commitments to human accountability, the protection of confidential submissions, and disclosure of consequential assistance, while offering different recommendations on assistance with research ideas and proofs, disclosure requirements, automated review, and policy enforcement. By distinguishing shared principles from unresolved implementation questions, the article clarifies the choices facing statistical publishing and outlines an agenda for evaluating policies and practices as GenAI evolves. The account seeks to foster continued discussion of GenAI in scientific communication and encourage statistical organizations to develop more robust operational standards.

发表机构

• University of Connecticut（康涅狄格大学）
• Harvard University（哈佛大学）
• National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
• University of Maryland（马里兰大学）
• University of Miami（迈阿密大学）
• Rutgers, The State University of New Jersey（罗格斯，新泽西州立大学）

机构由 AI 辅助整理，请以论文原文为准。