Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary?
Open accessAbstract
Currently there is a broad consensus among scholars that artificial intelligence (AI) tools can be used in research and publication, and that their use should be disclosed. Publishers and influential organizations, like the International Committee of Medical Journal Editors, have developed different and sometimes contradictory disclosure policies. We review some of these policies, examine the ethical reasons for disclosing AI use in research, and develop a framework for disclosure. We distinguish between mandatory, optional, and unnecessary disclosure of AI use, arguing that disclosure should be mandatory only when AI use is intentional and substantial. AI use is intentional when it is directly employed with a specific goal or purpose in mind. AI use is substantial when it 1) produces evidence, analysis, or discussion that supports or elaborates on the conclusions/findings of a study; or 2) directly affects the content of the research/publication. To support the application of our framework, we state three criteria for identifying substantial AI uses in research: a) using AI to make decisions that directly affect research results; b) using AI to generate content, data or images; and c) using AI to analyze content, data or images. Disclosure should be mandatory when AI use meets one of these criteria.
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Resnik and Hosseini define the criteria for mandatory disclosure as 'intentional and substantial use.'
“They define the criteria for mandatory disclosure as “intentional and substantial use.””
From Defining the Boundaries of AI Use in Scientific Writing: A Comparative Review of Editorial Policies (2025) · cites this paper as [23]
Given sensitivity of retraction process and potential for false positives when using AI to screen articles for retraction, medical community became hesitant to use AI for this purpose.
“Given the sensitivity of the retraction process and the potential for false positives when using AI to screen articles for retraction, the medical community became hesitant to use AI for this purpose.46”
From Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors (2025) · cites this paper as [46]
The Introduction discusses acceptance of AI for research tasks and policies for disclosing its use; the section on visibility to peer reviewers discusses bias in manuscript assessment. Neither addresses AI screening of articles for retraction, false positives in that screening, or resulting hesitation in the medical community. Searches for those terms found no relevant passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredResnik and Hosseini categorize the disclosure of AI usage into three levels-mandatory, optional, and unnecessary-based on ethical principles.
“Resnik and Hosseini23 categorize the disclosure of AI usage into three levels—mandatory, optional, and unnecessary—based on ethical principles.”
From Defining the Boundaries of AI Use in Scientific Writing: A Comparative Review of Editorial Policies (2025) · cites this paper as [23]
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References 25 · Cited by 104
Research works printed but not linked: 25
- Abernethy NJ (2024). Let stochastic parrots squawk: why academic journals should allow large language models to coauthor articles. AI Ethics (2024). 10.1007/s43681-024-00575-7 · DOI
- Ali AE, Venkatraj KP, Morosoli S, Naudts L, Helberger N, and Cesar P (2024). Transparent AI disclosure obligations: who, what, when, where, why, how. CHI EA '24: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. Article no. 342, pp. 1 – 11. 10.1145/3613905.3650750 · DOI
- Amano T, Ramírez-Castañeda V, Berdejo-Espinola V, Borokini I, Chowdhury S, Golivets M, González-Trujillo JD, Montaño-Centellas F, Paudel K, White RL, and Veríssimo D. (2023). The manifold costs of being a non-native English speaker in science. PLOS Biology, 21(7), e3002184. 10.1371/journal.pbio.3002184 37463136 PMC10353817 · DOI
- Ball P (2023). Is AI leading to a reproducibility crisis in science? Nature, December 5, 2023. Available at: https://www.nature.com/articles/d41586-023-03817-6
- Fabiano N, Gupta A, Bhambra N, Luu B, Wong S, Maaz, Fiedorowicz JG, Smith AL, and Solmi M (2024). How to optimize the systematic review process using AI tools. JCPP Advances 4(2): e12234. 10.1002/jcv2.12234 38827982 PMC11143948 · DOI · Paper page
- Heliyon (n.d.). Guide for authors. Available at: https://www.cell.com/heliyon/guide-for-authors (accessed February 11, 2025)
- Hadan H, Wang DM, Mogavi RH, Tu J, Zhang-Kennedy L, and Nacke LE. (2024). The great AI witch hunt: Reviewers’ perception and (Mis)conception of generative AI in research writing. Computers in Human Behavior: Artificial Humans, 2(2), 100095. 10.1016/j.chbah.2024.100095 · DOI
- Hosseini M and Lewis J (2020). The norms of authorship credit: challenging the definition of authorship in the European Code of Conduct for Research Integrity. Accountability in Research 27(2): 80– 98. 10.1080/08989621.2020.1721288 31976754 · DOI
- ICMJE (International Committee of Medical Journal Editors) (2023). Defining the roles of authors and contributions. Available at: https://www.icmje.org/news-and-editorials/updated_recommendations_may2023.html (accessed February 14, 2025).
- ICMJE (International Committee of Medical Journal Editors) (2024). Defining the roles of authors and contributions. Available at: https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html (accessed November 20, 2024).
- Jenkins R and Lin P (2023). AI-assisted authorship: how to assign credit in synthetic scholarship. Available at: 10.2139/ssrn.4342909 (accessed November 20, 2024). · DOI
- Kaebnick GE, Magnus DC, Kao A, Hosseini M, Resnik D, Dubljević V, Rentmeester C, Gordijn B, and Cherry MJ (2023). Editors’ statement on the responsible use of generative AI technologies in scholarly journal publishing. The Hastings Center Report 53(5): 3– 6. PMC11917467 37777997
- Mann PS, Vazirani AA, Aboy M, Earp BD, Minssen T, Cohen IG, and Savulescu J (2024). Guidelines for ethical use and acknowledgement of large language models in academic writing. Nature Machine Intelligence. 10.1038/s42256-024-00922-7 · DOI · Paper page
- Resnik DB, Tyler AM, Black JR, and Kissling GE (2016). Authorship policies of scientific journals. Journal of Medical Ethics 42: 199– 202. 10.1136/medethics-2015-103171 26714812 PMC4769679 · DOI
- Smith E, Williams-Jones B, Master Z, Larivière V, Sugimoto CR, Paul-Hus A, Shi M, and Resnik DB (2020). Misconduct and misbehavior related to authorship disagreements in collaborative science. Science and Engineering Ethics 26(4): 1967– 1993. 31161378 PMC6888995
- Smith OM, Davis KL, Pizza RB, Waterman R, Dobson KC, Foster B, Jarvey JC, Jones LN, Leuenberger W, Nourn N, Conway EE, Fiser CM, Hansen ZA, Hristova A, Mack C, Saunders AN, Utley OJ, Young ML, and Davis CL. (2023). Peer review perpetuates barriers for historically excluded groups. Nature Ecology & Evolution, 7(4), 512– 523. 10.1038/s41559-023-01999-w 36914773 · DOI
- Springer (2023). Editorial Policies ∣ Artificial Intelligence (AI). Available at: https://www.springer.com/gp/editorial-policies/artificial-intelligence--ai-/25428500 (accessed November 20, 2024).
- Suchikovaa Y and Tsybuliak N (2024). ChatGPT isn’t an author, but a contribution taxonomy is needed. Accountability in Research. 10.1080/08989621.2024.2405039 39292004 · DOI
- Taylor & Francis (2024). AI Policy. Available at: https://taylorandfrancis.com/our-policies/ai-policy/ (accessed November 20, 2024).
- Thorp HH and Vinson V (2023). Change to policy on the use of generative AI and large language models. Science Editor’s Blog. Available at: https://www.science.org/content/blog-post/change-policy-use-generative-ai-and-large-language-models (accessed November 20, 2024)
- Vilhekar RS and Rawekar A (2024). Artificial intelligence in genetics. Cureus 16(1): e52035. 10.7759/cureus.52035 38344556 PMC10856672 · DOI
- World Association of Medical Editors (WAME) (2023). Chatbots, generative AI, and scholarly manuscripts. Available at: https://wame.org/page3.php?id=106 (accessed November 20, 2024). 37523731
- Zerilli J, Bhatt U, and Weller A (2022). How transparency modulates trust in artificial intelligence. Patterns 3(4): 100455. 10.1016/j.patter.2022.100455 35465233 PMC9023880 · DOI · Paper page
- Zielinski C, Winker M, Aggarwal R, Ferris L, Heinemann MK, Lapeña Florencio J, Pai S, Ing E, and Citrome L. (2023a, January 20). WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications. https://wame.org/page3.php?id=110 37615142
- Zielinski C, Winker M, Aggarwal R, Ferris L, Heinemann MK, Lapeña Florencio J, Pai S, Ing E, Citrome L, Murad A, Voight M, and Habibzadeh F. (2023b, May 31). WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications. https://wame.org/page3.php?id=106 PMC10712422 38089825
- Authors
- Resnik, D. B., & Hosseini, M.
- Affiliations
- National Institutes of Health (US)
- Northwestern University (US)
- Corresponding author
- Hosseini, Mohammad
- Venue
- Accountability in Research
- Publisher
- Informa UK Limited
- Published
- 2025-03-24
- Volume
- 33
- Issue
- 2
- Pages
- 2481949-2481949
- ISSN
- 0898-9621 (ISSN-L)
- 1545-5815
- PMC
- PMC12353913
- OpenAlex
- W4408808982
- Cited by
- 104
- References
- 25
- Citations by year
- 2026: 74 · 2025: 30
- FWCI
- 107.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Accountability in Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Intramural Research Program (The intramural program of the NIH.)
- National Center for Advancing Translational Sciences (UM1TR005121)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Keywords
- AI disclosure, AI use in research, disclosure requirements, disclosure policy, ICMJE, research ethics, AI-generated content, AI-generated data, AI-generated images, disclosure framework
- MeSH terms
- Artificial Intelligence, Humans, Publishing, Disclosure, Biomedical Research
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