# Expectations and Practices around AI Disclosure in CS Research

arXiv: https://arxiv.org/abs/2608.23271 · HTML: https://mlsanity.com/paper/2608.23271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions](https://mlsanity.com/paper/2411.05025/) (2024)
- [Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI](https://mlsanity.com/paper/2303.08931/) (2023)
- ["I'm categorizing LLM as a productivity tool": Examining ethics of LLM use in HCI research practices](https://mlsanity.com/paper/2403.19876/) (2024)
- [Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace](https://mlsanity.com/paper/2410.18334/) (2024)
- [Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writing](https://mlsanity.com/paper/2410.11977/) (2024)

## Abstract (arXiv)

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 is frequently disclosed. We conclude with recommendations to align AI disclosure policies and practices with expectations, suggesting a categorization of research tasks by perceived necessity and a boilerplate template capturing expected details.