The AIR framework for research transparency: a critical analysis of stage-specific AI disclosure in the context of accessibility and research integrity

Abstract As generative AI tools integrate rapidly into research workflows, the absence of shared disclosure vocabulary creates what I characterize as a transparency crisis: researchers wish to use AI responsibly but lack consistent language for describing how these tools contribute to their work. The AIR (AI in Research) framework addresses this gap through a two-dimensional matrix mapping AI involvement across seven research stages and five engagement bands, from no use to substantial use. This critical review examines AIR’s theoretical foundations, empirical viability, and ethical limitations, with particular attention to accessibility and neurodiversity. Drawing on virtue epistemology, I argue that transparency must be understood as a constitutive epistemic virtue rather than a procedural requirement, and assess how AIR operationalizes this commitment. An inter-rater reliability pilot study ( n = 15 raters, nine scenarios, Cohen’s κ = 0.72) demonstrates that trained evaluators can apply AIR with substantial agreement while revealing systematic boundary ambiguities. Critical analysis identifies five major limitations: false precision in ambiguous practices, inadequate treatment of accessibility-related AI use, stigmatization of legitimate high-band practices, vulnerability to adversarial compliance, and insufficient edge case guidance. I propose evidence-informed refinements including boundary case designations, a protected A1-Access sub-band for disability accommodations, separation of verification burden from appropriateness judgment, spot-check validation studies, and community-maintained edge case repositories. These refinements aim to preserve AIR’s descriptive precision while protecting vulnerable researchers and mitigating exclusion risks. The analysis finds that AIR, with the proposed refinements, shows promise as transparency infrastructure, but that implementation requires sustained dialogue among researchers, integrity officers, editors, accessibility advocates, and policymakers to ensure research integrity and inclusion remain interdependent rather than competing aspirations.

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Publication Details

Journal
AI & Society
Published
2026-05-16
DOI
https://doi.org/10.1007/s00146-026-03082-x
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

The AIR framework for research transparency: a critical analysis of stage-specific AI disclosure in the context of accessibility and research integrity

David Ruttenberg
AI & Society
Ethics and Social Impacts of AI
article

The AIR framework for research transparency: a critical analysis of stage-specific AI disclosure in the context of accessibility and research integrity

David Ruttenberg
article en

Abstract

Abstract As generative AI tools integrate rapidly into research workflows, the absence of shared disclosure vocabulary creates what I characterize as a transparency crisis: researchers wish to use AI responsibly but lack consistent language for describing how these tools contribute to their work. The AIR (AI in Research) framework addresses this gap through a two-dimensional matrix mapping AI involvement across seven research stages and five engagement bands, from no use to substantial use. This critical review examines AIR’s theoretical foundations, empirical viability, and ethical limitations, with particular attention to accessibility and neurodiversity. Drawing on virtue epistemology, I argue that transparency must be understood as a constitutive epistemic virtue rather than a procedural requirement, and assess how AIR operationalizes this commitment. An inter-rater reliability pilot study ( n = 15 raters, nine scenarios, Cohen’s κ = 0.72) demonstrates that trained evaluators can apply AIR with substantial agreement while revealing systematic boundary ambiguities. Critical analysis identifies five major limitations: false precision in ambiguous practices, inadequate treatment of accessibility-related AI use, stigmatization of legitimate high-band practices, vulnerability to adversarial compliance, and insufficient edge case guidance. I propose evidence-informed refinements including boundary case designations, a protected A1-Access sub-band for disability accommodations, separation of verification burden from appropriateness judgment, spot-check validation studies, and community-maintained edge case repositories. These refinements aim to preserve AIR’s descriptive precision while protecting vulnerable researchers and mitigating exclusion risks. The analysis finds that AIR, with the proposed refinements, shows promise as transparency infrastructure, but that implementation requires sustained dialogue among researchers, integrity officers, editors, accessibility advocates, and policymakers to ensure research integrity and inclusion remain interdependent rather than competing aspirations.

AI & Society
Openalex Percentile: Top 4%
Ethics and Social Impacts of AI
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