Generative AI in Publishing: An Editors' Panel on Ethics and Policies

Generative AI is transforming the way researchers draft, edit, and even conceptualize academic work, yet there is little consensus on acceptable practices in publishing. Editors are actively shaping policies now, and the lack of uniformity creates uncertainty for authors and reviewers alike. This panel brings together editorial leaders to address these pressing questions at a critical juncture. This panel will focus on the ethical and policy challenges of publishing scholarly papers prepared with the aid of generative AI. Panelists will be asked to outline the range of editorial policies that currently exist, which span from restrictive approaches allowing only light language editing to more permissive stances that accept uses such as literature review support or exploratory data analysis. The panel will also address emerging norms around authorship and accountability, where most policies specify that generative AI cannot be listed as a coauthor and that human authors are expected to take full responsibility for the accuracy and integrity of the work. A third theme will be transparency, particularly the requirement that the use of generative AI be disclosed at the point of submission, and the difficulties of defining and enforcing meaningful disclosure. Finally, the panelists will look ahead to future directions, including the possibility of harmonizing policies across journals, developing best practices, and addressing new ethical questions as AI tools evolve. The panelists are: Paul Albert, Editor-in-Chief of Statistics in Medicine, Director of Biostatistics Branch, National Cancer Institute. Yulin Hswen, Associate Editor of JAMA and JAMA Network, Leader of JAMA+ AI, Associate Professor of Epidemiology and Biostatistics and at the University of California, San Francisco. Lan Wang, Former Co-Editor of the Annals of Statistics, Centennial Endowed Chair Professor of Management Science, University of Miami. Minge Xie, Editor of The American Statistician, Co-Editor-in-Chief of The New England Journal of Statistics in Data Science (NEJSDS), Distinguished Professor of Statistics, Rutgers University. Jun Yan, Editor of the Journal of Data Science, Professor of Statistics, University of Connecticut.