Regulation and Editorial Policies on the Use of Artificial Intelligence in Open Science Publishing

The use of artificial intelligence (AI) in scholarly publishing has expanded rapidly, generating new ethical, technical, and regulatory challenges. Within the framework of open science, these technologies offer opportunities to streamline scientific production, improve editorial processes, and broaden access to knowledge. However, they also raise concerns related to responsible authorship, algorithmic transparency, and the integrity of research outputs. This poster presents a review of recent editorial policies and guidelines regarding the use of AI in scholarly communication, with an emphasis on open access environments. Among the most relevant findings is a growing consensus that AI tools (especially generative models) should not be listed as authors. Furthermore, full responsibility for the content lies with the human authors, who must explicitly disclose any use of AI and ensure the validity and originality of the final text. There is also a notable trend among scientific journals to demand greater transparency regarding the provenance of data and the behavior of the models used. The goal is to foster critical reflection on how to design ethical and inclusive editorial policies that align with open science principles, enabling the benefits of AI without compromising quality, equity, or accountability in scholarly communication.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-21
DOI
https://doi.org/10.5281/zenodo.17671724
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Regulation and Editorial Policies on the Use of Artificial Intelligence in Open Science Publishing

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

Regulation and Editorial Policies on the Use of Artificial Intelligence in Open Science Publishing

article en

Abstract

The use of artificial intelligence (AI) in scholarly publishing has expanded rapidly, generating new ethical, technical, and regulatory challenges. Within the framework of open science, these technologies offer opportunities to streamline scientific production, improve editorial processes, and broaden access to knowledge. However, they also raise concerns related to responsible authorship, algorithmic transparency, and the integrity of research outputs. This poster presents a review of recent editorial policies and guidelines regarding the use of AI in scholarly communication, with an emphasis on open access environments. Among the most relevant findings is a growing consensus that AI tools (especially generative models) should not be listed as authors. Furthermore, full responsibility for the content lies with the human authors, who must explicitly disclose any use of AI and ensure the validity and originality of the final text. There is also a notable trend among scientific journals to demand greater transparency regarding the provenance of data and the behavior of the models used. The goal is to foster critical reflection on how to design ethical and inclusive editorial policies that align with open science principles, enabling the benefits of AI without compromising quality, equity, or accountability in scholarly communication.

Zenodo (CERN European Organization for Nuclear Research)
Instituto Tecnológico de Costa Rica (CR)
Openalex Percentile: Top 48%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.