Against mandatory prompt disclosure in AI-assisted scholarship

Abstract Large language models (LLMs) are becoming routine as tools for drafting, editing, and revising scholarly manuscripts, raising difficult questions about how much transparency is needed to assess research integrity, authorship claims, and accountability. In response, some authors have proposed that researchers who use LLMs in manuscript preparation should be required to submit the prompts they used, together with the generated outputs, as supplemental material. We support detailed disclosure when it is needed to assess the scientific validity or reproducibility of a study, for example where LLMs are used in data analysis, coding, literature synthesis, or the production of figures. We argue, however, against Mandatory Full Inclusion (MFI) of prompts and outputs for ordinary writing assistance. Such a policy would, we suggest, be difficult to enforce, easy for dishonest users to evade, burdensome for careful users to follow, and poorly suited to the capacities of editors and peer reviewers. It would also risk discouraging beneficial uses of LLMs, penalizing careful multi-model consultation, distorting how researchers interact with AI systems, and disadvantaging scholars who already face undue or unnecessary barriers in academic publishing, including scholars at under-resourced institutions. More generally, an MFI policy would impose a level of surveillance on AI-assisted writing that has no clear analog in ordinary human collaboration. We propose instead a proportionate prompt-and-output disclosure framework: targeted disclosure where reproducibility or validity requires it; voluntary documentation where authors wish to demonstrate their contribution; and continued reliance on authorship attestation, contributor statements, and misconduct procedures where warranted. This approach better aligns transparency with the risks posed by LLM use in scholarly writing, while also minimizing surveillance-related harms and inefficiencies.