Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: University of Connecticut, Harvard University, National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics, University of Maryland, Baltimore, University of Maryland, College Park, University of Miami, Rutgers, The State University of New Jersey
Language: English
Subfield: Library and Information Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7220939380