Editors' statement on the responsible use of generative artificial intelligence technologies in scholarly journal publishing

Abstract

The new generative artificial intelligence (AI) tools, and especially the large language models (LLMs) of which ChatGPT is the most prominent example, have the potential to transform many aspects of scholarly publishing. How the transformations will play out remains to be seen, both because the different parties involved in the production and publication of scholarly work are still learning about these tools and because the tools themselves are still in development, but the tools have a vast range of potential uses. Authors are likely to use generative AI to conduct research, frame their thoughts, produce data, search for ways of articulating their thoughts, develop drafts, generate text, revise their writing, and create visuals. Peer reviewers might use AI to help them produce their reviews. Editors might use AI in the initial editorial screening of manuscripts, to locate reviewers, or for copyediting.

We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. We do not pretend to have resolved the many social questions that we think generative AI raises for scholarly publishing, but in the interest of fostering a wider conversation about these questions, we have developed a preliminary set of recommendations about generative AI in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work.

The stance set out here is consistent with those taken by the Committee on Publishing Ethics and many journal publishers, including those that publish or provide publishing services to the journals we edit. Previous position statements have addressed concerns about the use of AI for peer review and the importance of reviewers revealing to authors if they used AI in their review.5 However, to our knowledge, none have addressed the importance of using human reviewers to review manuscripts and editors retaining final decisions over what reviewers to select. Our stance differs from the position of Science magazine, which holds not only that a generative AI tool cannot be an author but also that “text generated by ChatGPT (or any other AI tools) cannot be used in the work, nor can figures, images, or graphics be the products of such tools.”6 Such a proscription is too broad and may be impossible to enforce, in our view. Yet we recognize that the ethical issues raised by generative AI are complex, and we have struggled to decide how editors should promote responsible use of these technologies. Over time, we hope, the community of scholars will develop professional norms about the appropriate ways of using these new tools. Reviewers and readers, not just editors, will have much to say about these norms. The variety of ways in which generative AI technologies can be used and the pace of change may, in fact, render detailed editorial policy statements ineffective or impracticable. Instead, reliance on evolving professional norms based on broader public conversation about generative AI technologies may turn out to be the best way forward. Our shared statement is intended to promote this wider social discourse.

David Resnik's contribution to this editorial was supported by the Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS) at the National Institutes of Health (NIH). Mohammad Hosseini's contribution was supported by the National Center for Advancing Translational Sciences (NCATS) (through grant UL1TR001422). The funders have not played a role in the design, analysis, decision to publish, or preparation of the manuscript. Veljko Dubljević's contribution was partially supported by the National Science Foundation (NSF) CAREER award (#2043612). This work does not represent the views of the NIEHS, NCATS, NIH, NSF, or US government.