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.
BACKGROUND:This paper examines responses to data-sharing requests from authors in the Social Sciences. Compliance with requests for data-sharing in Medical Sciences has been reported to be low. The study extends the scholarship by assessing compliance with similar requests in the Social Sciences. METHODS:Four hundred empirical study authors from 400 Q1 Scopus-indexed Journals across eight prominent fields of the Social Sciences were requested to share the research data. Only those authors were contacted who had stated that data would be made available on request, or those who had not explicitly denied data-sharing. RESULTS:Sixty-seven authors responded, of whom 15 (4%) provided data, while 52 declined to share data on grounds such as privacy concerns. The response rate of 4% was far lower than the average of 12% reported in Medical Sciences. CONCLUSIONS:While policy encourages data-sharing, practice has yet to catch up. Existing literature indicates various reasons for not sharing research data. These include unavailability, privacy concerns, ethical concerns, lack of publisher compulsion, and others. It is important to address the issue of authors not responding to requests despite a promise. Policymakers also need to examine this issue to identify ways to improve data-sharing and promote open science.
BACKGROUND:Despite the proliferation of AI disclosure requirements in academic publishing, recent research suggests a persistent gap between policy expectations and research practice. However, little is known about how researchers perceive and navigate these requirements or what limitations they identify in current disclosure practices. METHOD:This study explored researchers' experiences with AI disclosure through semi-structured interviews with 14 researchers from two interdisciplinary fields, bioinformatics and computational social science. Data were analyzed using reflexive thematic analysis. RESULTS:Four thematic groupings emerged: fragmented and inconsistently enforced requirements; systemic limitations, including scope ambiguity, research integrity risks, and structural disincentives to honest reporting; researcher perspectives on more effective disclosure practices; and disciplinary variation as a cross-cutting dimension shaping how these issues are experienced across research communities. The findings suggest that the compliance gap reflects an interaction between structural conditions and ethical obligations. This gap is sustained by self-reporting mechanisms that lack verification capacity, a transparency paradox in which honest disclosure can invite professional penalization, and disciplinary norms that resist uniform governance approaches. CONCLUSIONS:The study provides empirical evidence supporting the development of a structured AI contribution taxonomy as a more principled and practical alternative to existing disclosure practices. More broadly, the findings suggest that effective AI disclosure governance should incorporate field-sensitive adaptation rather than relying on uniform implementation across diverse research communities.
