[V] Policies on Artificial Intelligence Among Academic Publishers
Notice bibliographique
Résumé
Jeremy Y. Ng,<sup>1,2,3</sup> Daivat Bhavsar,<sup>4</sup> Laura Duffy,<sup>4</sup> Hamin Jo,<sup>4</sup> Cynthia Lokker,<sup>4</sup> R. Brian Haynes,<sup>4,5</sup> Alfonso Iorio,<sup>4,5</sup> Ana Marušić<sup>6</sup> <h4>Objective </h4> This study examined the policies implemented by academic publishers regarding authors’ use of generative artificial intelligence (GenAI) tools, focusing on their regulation, disclosure requirements, and role in ensuring the integrity of scientific publications. By analyzing the prevalence and content of these policies, this study aimed to provide insight into the current landscape and inform future policy development in the rapidly evolving field of artificial intelligence (AI)–assisted research and publication. <h4>Design </h4> A cross-sectional audit was conducted on the publicly available policies of 163 academic publishers listed as members of the International Association of Scientific, Technical, and Medical Publishers. Policies were collected and analyzed between September 1 and December 31, 2023. Publishers without publicly accessible policies specific to GenAI use by authors were excluded. Data extraction and analysis were conducted independently in duplicate, with a third reviewer resolving discrepancies. The key policy components analyzed included authorship accreditation, disclosure requirements, and permissions for tasks such as research methods, content generation, image creation, and proofreading. Descriptive statistics were used to summarize the findings. Our protocol was registered.<sup>1</sup> <h4>Results </h4> Of 163 academic publishers, 56 (34.4%) had publicly available policies guiding GenAI use by authors. None permitted authorship accreditation for AI tools, citing accountability concerns and alignment with ethical guidelines. Nearly all publishers with policies (49 of 56 [87.5%]) mandated disclosure of GenAI use, primarily in the Methods or Acknowledgments section. However, disclosure practices varied, with some publishers providing standardized templates while others left requirements vague. Four publishers completely prohibited GenAI use in manuscript preparation, while others allowed their use for specific tasks. Most (33 of 56 [58.9%]) publishers permitted GenAI for drafting nonmethodological sections (eg, Introductions), while 18 (32.1%) permitted their use in research methods, such as data analysis and organization. Few publishers addressed GenAI use in image generation (14 of 163 [8.6%]) or proofreading (15 of 163 [9.2%]). Only 1 publisher (0.6%) allowed citation of AI as primary sources, while 19 (11.6%) explicitly prohibited such citations. Our study has been published.<sup>2</sup> <h4>Conclusions </h4> This audit highlights the inconsistent development of GenAI policies among academic publishers, with large variability in scope and clarity. While the prohibition of AI authorship and the emphasis on mandatory disclosure are consistent themes, inconsistencies in regulating specific tasks suggest a need for standardized and comprehensive policies. As AI technology and its applications in research evolve, publishers must adapt to safeguard scientific integrity. Given that the AI landscape is fast moving, future research includes updating this audit and comparing and contrasting current policies with those found in this study. Future research should also assess how policies are implemented and enforced by examining samples of published articles, as well as explore how these policies affect editors and reviewers, taking into account potential risks, such as privacy breaches and bias. <h4>References</h4> 1. Bhavsar D, Lokker C, Haynes RB, Iorio A, Marusic A, Ng JY. Academic publisher artificial intelligence chatbot policies for authors: a cross-sectional audit. OSF Registries. Accessed July 11, 2025. doi:10.17605/OSF.IO/937ES <span lang="fr-FR">2. Bhavsar D, Duffy L, Jo H, et al. Policies on artificial intelligence chatbots among academic publishers: a cross-sectional audit. </span><i>Res Integr Peer Rev</i><span lang="fr-FR">. 2025;10(1):1. </span>doi:10.1186/s41073-025-00158-y <sup>1</sup>Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany, jeremyyng.phd@gmail.com; <sup>2</sup>Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany; <sup>3</sup>Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada; <sup>4</sup>Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; <sup>5</sup>Department of Medicine, McMaster University, Hamilton, Ontario, Canada; <sup>6</sup>Department of Research in Biomedicine and Health and Center for Evidence-Based Medicine, University of Split School of Medicine, Split, Croatia. <h4>Conflicts of Interest Disclosures </h4> The authors declare no conflicts of interest. Ana Marušić is a member of the Peer Review Congress Advisory Board but was not involved in the review or decision for this abstract.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,013 | 0,021 |
| Études des sciences et des technologies | 0,002 | 0,030 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,012 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,035 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».