Disclosure rules fall short for responsible AI use in research
When disclosure rules are not enough: authorial warrant and judgment in ai-supported scholarship
Computers and Society
Summary
Many research journals now require authors to say when they use AI tools, but simply saying you used AI doesn't explain how the AI's suggestions affected the author's own thinking or judgment. The authors argue that researchers need to clearly show they understand and can defend the claims they make, even when AI helped. They suggest separating cases where AI changes important decisions from routine help that doesn't affect judgment. This helps ensure that humans remain responsible for what they write and prevents over-relying on AI without critical evaluation.
What this means in practice
- •For journal editors: Improve editorial policy by including criteria on how claims influenced by AI should be evaluated and defended by authors.
- •For academic publishers: Develop guidelines that distinguish routine AI assistance from substantive cognitive delegation to maintain author responsibility.
A position paper. It proposes an approach and reports no results.
Authors
Marcin Mirończuk
Abstract
Editorial policies increasingly require authors to disclose substantive uses of generative AI, but such requirements commonly focus on whether, where, and how a model was used. Even function-specific disclosure may locate a model in the research or writing process without showing how its outputs entered scholarly judgment, how they were evaluated, or whether contributors can reconstruct the relevant reasoning and defend resulting claims without treating those outputs as sufficient justification. Drawing on research ethics, AI disclosure policy, human-model interaction, and researcher formation, this article argues that disclosure documents model use, whereas responsible use requires claim-level authorial warrant: a contributor's capacity to understand, justify, and defend claims within their contribution. The article distinguishes cognitive delegation, in which model outputs shape substantive choices, interpretations, or justifications, from routine delegation, which does not ordinarily bear on the substance of judgment. Its conceptual contribution is to link the epistemic role of outputs that shape judgment to the function-appropriate evaluation required to justify decisions and retain warrant for resulting claims. It then repurposes disclosure categories as a claim-sensitive ex ante heuristic, mapping six recurrent research functions to risks, forms of oversight, and formative questions for recognising cognitive delegation and selecting function-appropriate evaluation. The article contributes to the ethics of information technology by clarifying when AI-supported scholarship remains under responsible human judgment and when cognitive delegation threatens the epistemic basis of authorship.