Expectations and Practices around AI Disclosure in CS Research
2026-08-24 • Computers and Society
Computers and SocietyComputation and LanguageHuman-Computer Interaction
AI summaryⓘ
The authors studied how researchers talk about using AI tools in their work, especially in computer science. They found that the rules for sharing when AI is used are unclear. By surveying researchers, they learned people think it's most important to mention AI when it helps with designing research or when humans do little work. However, by looking at many real examples, the authors saw that what researchers share about AI use often doesn’t match these ideas, like AI help with writing being shared a lot even though it's considered less important to disclose. They suggest better guidelines to make AI use sharing clearer and more useful.
generative AIAI disclosure policiesresearch workflowscomputer science researchsurvey studyEMNLP conferenceICLR conferencehuman involvementwriting assistancepolicy recommendations
Authors
Arati Mohapatra, Danish Pruthi
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.