Funding agencies say no to AI peer review
Abstract
Concerns include confidentiality, accuracy, and “originality of thought”
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Publishers and funding agencies have put out various policies banning LLMs in peer review, as coauthors, or as an undisclosed resource of any kind.
“publishers and funding agencies have put out various policies, banning LLMs in peer review (49, 50), as coauthors (51), or undisclosed resource of any kind (50).”
From Delving into LLM-assisted writing in biomedical publications through excess vocabulary (2025) · cites this paper as [49]
Cited by
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Delving into LLM-assisted writing in biomedical publications through excess vocabulary
Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2024) · arXiv · doi:10.48550/arxiv.2406.07016 · Cited by 214
Delving into LLM-assisted writing in biomedical publications through excess vocabulary
Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2025) · Science Advances · doi:10.1126/sciadv.adt3813 · Cited by 213 · see citing works
“As a result, publishers and funding agencies have put out various policies, banning LLMs in peer review (Kaiser, 2023; Brainard, 2023), as co-authors (Thorp, 2023), or undis-closed resource of any kind (Brainard, 2023).”
Zhou, Y., Liu, Q., Huang, J., & Li, G. (2026) · Technology in Society · doi:10.1016/j.techsoc.2025.103087 · Cited by 24 · see citing works
AI-powered peer review needs human supervision
Seghier, M. L. (2025) · Journal of Information, Communication and Ethics in Society · doi:10.1108/jices-09-2024-0132 · Cited by 18 · see citing works
Carbonell Cortés, C., Parra-Rojas, C., Pérez-Lozano, A., Arcara, F., Vargas-Sánchez, S., Fernández-Montenegro, R., Casado-Marín, D., Rondelli, B., & López-Verdeguer, I. (2024) · Data & Policy · doi:10.1017/dap.2024.41 · Cited by 15 · see citing works
“…of funders, agencies such as the National Institutes of Health 12 in the US and the Australian Research Council 13 have prohibited the use of generative AI tools to analyze and formulate peer reviews due to concerns about factual accuracy, breaches of confidentiality, and biases (Kaiser, 2023).”
Leopold, S. S., Briars, C. E., Gebhardt, M. C., Gioe, T. J., Manner, P. A., Porcher, R., Rimnac, C. M., & Wongworawat, M. D. (2024) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003084 · Cited by 10 · see citing works
AI in peer review: Publishing’s panacea or a pandora’s box of problems?
Nath, K. A., Conway, M., & Fonseca, R. (2024) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2023.11.013 · Cited by 10 · see citing works
Alternative models of funding curiosity-driven research
Gigerenzer, G., Allen, C., Gaillard, S. D. M., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401237121 · Cited by 7 · see citing works
LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions
Liao, Z., Antoniak, M., Cheong, I., Cheng, E. Y.-Y., Lee, A.-H., Lo, K., Chang, J. C., & Zhang, A. X. (2024) · arXiv · doi:10.48550/arxiv.2411.05025 · Cited by 7 · see citing works
The Controversy behind the Controversies: Scientific Discourse in the Twenty-First Century
Fahnestock, J. (2025) · Rhetoric Society Quarterly · doi:10.1080/02773945.2025.2484162 · Cited by 5 · see citing works
Scientific Peer Review in an Era of Artificial Intelligence
Kadri, S., Dorri, N., Osaiweran, M., Garyali, P., & Petković, M. (2024) · Springer eBooks · doi:10.1007/978-981-97-4060-4_23 · Cited by 4 · see citing works
Bakker, T. C. M., & Traniello, J. F. A. (2023) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-023-03416-5 · Cited by 2 · see citing works
“It is agreed that ChatGPT and similar tools should be excluded as “authors” of scientific papers (Stokel-Walker and Van Noorden 2023) and in reviews submitted to funding agencies (Kaiser 2023).”
Responsible AI for Government Program Evaluation and Performance Audits
Fonner, D. F., & Coyle, F. P. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825518 · Cited by 1 · see citing works
“…the use of AI models for the evaluation of individual grant applications for “confiden-tiality, accuracy, and ‘originality of thought’” concerns [3], [4], the use of closed systems based on open models [5] provides a means by which agencies can evaluate the outcomes of funding programs relative…”
Woodhams, J. H., Parry, J. M., & Scott, J. A. G. (2025) · doi:10.37672/ukrio.2025.06.embracingaiwithintegrity · Cited by 1 · see citing works
Large Language Models in Academic Publishing
Thiga, M. M. (2023) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-0487-7.ch009
Bessa, J. C. R. (2024) · Diálogo das Letras · doi:10.22297/2316-17952024v13e02436
Design and Practice of Financial Reporting Model Based on ChatGPT
Wen, Y. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-06372-4_4
From AI peer review to AI ethics: addressing terminological confusion in science
Kozak, M. (2025) · Journal of Documentation · doi:10.1108/jd-07-2025-0201
- Authors
- Kaiser, J.
- Corresponding author
- Kaiser, Jocelyn
- Venue
- Science
- Published
- 2023-07-21
- Volume
- 381
- Issue
- 6655
- Pages
- 261-261
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4384924643
- Cited by
- 18
- References
- 0
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 7 · 2023: 2
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, peer review, research funding agencies, AI-generated research, research integrity, confidentiality, scholarly publishing
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