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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.