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This study investigates the misalignment between current AI disclosure policies in top computer science venues and researcher expectations regarding generative AI usage. The analysis reveals that while disclosures are frequently submitted for low-involvement tasks like writing assistance, researchers deem them most critical for research design phases with minimal human intervention. The findings offer actionable recommendations to refine disclosure standards, ensuring they accurately reflect the necessity of transparency across different levels of AI integration in scientific workflows.
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Expectations and Practices around AI Disclosure in CS Research

arXiv2 min read

arXiv:2608.23271v1 Announce Type: cross Abstract: As generative AI tools find increasing use in research workflows, ongoing debates on their impact, appropriateness and responsible use have led policymakers to enact policies to disclose AI use at multiple publishing venues. However, are current AI disclosure policies and practices reflective of their purpose? In this work, we first investigate disclosure policies of top computer science venues and find that despite their prevalence, they remain highly under-specified. Secondly, through a survey of computer science researchers (N=$109$), we characterize the necessity of disclosures across different research tasks and levels of human involvement. We learn that researchers find disclosures most necessary for tasks involving research design, and for tasks when the human involvement is low. We also compile expectations that researchers have about the information to be conveyed in AI disclosure statements. Lastly, through an analysis of $13867$ disclosure statements from EMNLP $2025$ and ICLR $2026$, we reveal a large disconnect between these expectations and AI disclosures in practice---a prime example being writing assistance which is deemed less necessary but frequently disclosed. We conclude with recommendations for authors and policymakers that seek to align AI disclosure policies and practices with expectations.

Abstract:As generative AI tools find increasing use in research workflows, ongoing debates on their impact, appropriateness and responsible use have led policymakers to enact policies to disclose AI use at multiple publishing venues. However, are current AI disclosure policies and practices reflective of their purpose? In this work, we first investigate disclosure policies of top computer science venues and find that despite their prevalence, they remain highly under-specified. Secondly, through a survey of computer science researchers (N=$109$), we characterize the necessity of disclosures across different research tasks and levels of human involvement. We learn that researchers find disclosures most necessary for tasks involving research design, and for tasks when the human involvement is low. We also compile expectations that researchers have about the information to be conveyed in AI disclosure statements. Lastly, through an analysis of $13867$ disclosure statements from EMNLP $2025$ and ICLR $2026$, we reveal a large disconnect between these expectations and AI disclosures in practice---a prime example being writing assistance which is deemed less necessary but frequently disclosed. We conclude with recommendations for authors and policymakers that seek to align AI disclosure policies and practices with expectations.

Computers and Society (cs.CY); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

arXiv:2608.23271 [cs.CY]

(or arXiv:2608.23271v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2608.23271 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 292 words

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