Generative AI in Publishing: An Editors' Panel on Ethics and Policies
Abstract
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.
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BibTeX
@misc{yan2026generative,
title = {{Generative AI in Publishing: An Editors' Panel on Ethics and Policies}},
author = {Jun Yan and Mark Glickman and Paul S. Albert and Yulin Hswen and Lan Wang and Minge Xie},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10224},
eprint = {2610.10224},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10224},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Generative AI in Publishing: An Editors' Panel on Ethics and Policies
AU - Jun Yan
AU - Mark Glickman
AU - Paul S. Albert
AU - Yulin Hswen
AU - Lan Wang
AU - Minge Xie
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10224
UR - https://arxiv.org/abs/2610.10224
AB - 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.
ER -