A researcher uses generative artificial intelligence (AI) to process data, draft a grant application or assemble a literature review. If the work is later challenged, investigators may need to reconstruct what the tool produced, what the researcher checked and whether the underlying records still exist. That is the practical challenge in new guidance from the Department of Health and Human Services’ Office of Research Integrity (ORI) on AI use in public health service-funded research.

ORI applies the existing research misconduct framework to generative AI. A finding requires a significant departure from accepted practices in the relevant research community, conduct committed intentionally, knowingly or recklessly, and proof by a preponderance of the evidence. The guidance offers nonbinding recommendations under revised regulations that became applicable to new allegations in January. AI creates no separate category of misconduct, but it complicates how institutions establish accepted practice, assess a researcher’s state of mind and preserve proof.

Researchers should identify the AI tools used in research and in preparing manuscripts and grants, and explain how they used them, ORI said. Disclosure can help establish reproducibility and defend against an allegation. It does not excuse conduct that significantly departs from accepted practice. Crowell & Moring, in an analysis of the guidance, advised institutions to document the inputs, outputs and verification steps as well as the tool’s name.

Determining accepted practice is itself a challenge. Research fields differ in their approaches to AI, while policies issued by funding agencies can also inform the standard. Crowell pointed to National Institutes of Health restrictions on grant applications “substantially developed” by AI and on peer reviewers using generative AI to analyze applications or prepare critiques.

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