Sari la conținut

Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchAI & societate

analiza aiqbench

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.
Surse care acoperăarXiv, LessWrong, Effective Altruism Forum, Fortune
Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

Expectations and Practices around AI Disclosure in CS Research

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

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)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Text integral sub licența deschisă a publicației · 292 cuvinte

Subiecte conexe