Subscription, open access, hybrid - and now AI: AI-induced evidence skew in scholarly work

Little attention has been paid to how publication and access models influence the evidence that AI systems retrieve, process, and summarize. This article introduces the concept of AI-induced evidence skew, proposes four pathways through which it may arise, and provides a preliminary empirical illustration.

Nine widely used large language models (LLMs) were prompted to generate literature-supported content. The accessibility status of the references provided was compared with the percentage of open-access records identified through searches of three scholarly databases.

AI-induced evidence skew is a structural distortion in AI-assisted evidence synthesis whereby outputs disproportionately represent open-access or otherwise machine-accessible literature. Four proposed pathways are differential representation of literature in training data, retrieval-access constraints, human verification practices, and system- or user-imposed access restrictions. Among 78 verifiable references generated by LLMs, a mean of 95.38% had freely available full text (87.43% were open-access), compared with a mean open-access percentage of 57.83% across scholarly database search results.

Although exploratory and non-generalizable, these findings support the hypothesis that AI-assisted search and synthesis may overrepresent publicly accessible literature. The concern is not the quality of open-access literature, but that accessibility may become an unintended determinant of included evidence. Mitigation requires greater awareness, human oversight, transparent reporting of AI use and retrieval limitations, verification of AI-generated references, and improved transparency regarding AI evidence coverage. Further empirical investigation across disciplines, platforms, and AI tools is warranted.