Pluto Journals AI Statement
Last updated: October 2026
1. AI Policy for Pluto Journals
Pluto Journals prioritises the value of human skill, creativity, and critical thinking in the scholarly ecosystem. We recognise the expertise required in every step of the publishing process. Our policies seek to ensure the successful production and distribution of work within the scholarly community, and to maintain the integrity of the scholarly record. Currently, Pluto Journals does not use AI tools in its production management or publication workflows, including work undertaken on its behalf by third-party service providers. Regarding the individual journals published by Pluto Journals, we adhere to the Committee on Publication Ethics’s position on artificial intelligence, while taking further cues from AI policies across the scholarly publishing ecosystem. Our policies are as follows:
1.1 Authorship
Across the spectrum, from commercial to noncommercial, publishers agree that AI cannot be listed as an author. Regardless of any contributions by an AI tool, the human authors remain responsible for the entire content of their published work and are liable for any breach of publication ethics, even if said breach was introduced by AI.
1.2 Transparency & Disclosure
Authors must publicly disclose any use of AI in their work, including but not limited to: data analysis, image and/or text generation, abstract and keyword creation, language translation, bibliographic citation creation, copyediting, etc. Disclosure must indicate which AI tools were used, and where/how they impacted the work.
1.3 Peer Review
AI cannot be employed in lieu of human feedback during the peer review process. AI cannot peer review work and make a recommendation for the acceptance, revision, or rejection of an article. Many databases furthermore restrict or exclude journals that utilise AI in the peer review process, which hinders Pluto Journal’s ability to index our journals. On the grounds of confidentiality and copyright, reviewers may not upload authors’ works to AI tools.
1.4 Production Services
In the interest of labour equity and quality control, fully AI-driven production workflows are not permitted in Pluto Journals journals. Copyediting must be performed by human professionals, not AI. If a journal wishes to incorporate AI tools during production, it must provide process documentation and sample content to Pluto Journals prior to implementing a new workflow. Should a journal proceed to utilise AI in their workflow (e.g., typesetting), this should be publicly disclosed on the journal’s webpage, either in the AI policy or in the submission guidelines. In addition, the journal must obtain consent from each author before uploading their work to any AI tools. Any AI-impacted elements must be subjected to the same level of human editorial scrutiny as any other published material–for example, if AI is used to translate an article into another language, the translated manuscript must be copyedited by a fluent reader of that language.
1.5 Individual Journal Policy
Each journal must develop their own AI policy to specify if and how AI may be used in the creation or publication of a work. This AI policy must be publicly available on the journal’s webpage and referenced in the publication agreements signed between the journal and authors. Journal editors may find it helpful to reference the Library Publishing Coalition’s AI Editorial Policy Guide while developing their policy. Note that GenAI images (or text samples) may be necessary as illustrative figures in articles that are specifically about AI/LLM technology. Even if other AI use is disallowed, it’s worth carving out an exception for scholarly uses like this.
2. Best Practices and Considerations
Because Pluto Journals does not have editorial authority over our journals, it is each journal’s prerogative to decide what to allow or disallow in the content that they choose to publish. We encourage all of our journals to take the following topics and resources into account when developing their own AI policies:
2.1 Integrity
Large Language Models, or LLMs, create content based on patterns and probabilities. They do not truly understand either the data they’ve ingested or the results they output, and they won’t necessarily generate the same result when given the same input. As a result, GenAI content is subject to myriad issues, including but not limited to: factual inaccuracies (e.g., reproducing erroneous data or misinterpreting context), biases (e.g., reinforcing systemic prejudices or distortions in data), and confabulations or “hallucinations” (e.g., fabricating information to fill gaps in data). We endeavor to support the integrity of the scholarly ecosystem—from authors to publishers to librarians to students and scholars—by ensuring human skill and review is present in every stage of the process. There is broad agreement across the scholarly publishing field on this point: in addition to many noncommercial presses, commercial publishers like Elsevier and Wiley also ensure integrity by restricting usage and providing guidelines for their peer review process.
2.2 Labour
We acknowledge that many of our colleagues find LLMs to be useful tools for coding, accessibility, and data analysis. However, we are concerned about the disruption to ethical labor practices caused by the adoption of generative AI tools. For example, from global workers in the AI industry to the publishing professionals whose jobs are at stake (e.g., translators, graphic designers), artificial intelligence causes many negative effects on the value and dignity of human labour.
2.3 Copyright
The question of copyright is subject to a fast-changing landscape. We stand on the side of author rights and encourage journals to stay abreast of rapid developments as they create their own policy and author agreements. LLMs are frequently trained on copyrighted materials, making it difficult to parse what is fair use, what may be considered plagiarism, etc., especially when content is generated entirely via AI. Furthermore, if genAI is utilised in the journal’s workflow (e.g., typesetting), it is possible that LLMs may incorporate the analysed text into their training data. For this reason, authors must consent to the use of AI in the workflow, per Pluto Journals policy.
2.4 Environment
The data centers powering GenAI pose a threat to our water systems and ecological health.
3. Considerations for Editors
With the expectation that some journals may choose to embrace AI to one degree or another, we have drafted a series of recommended best practices in addition to our official policy. Regardless of the extent of AI use, human review is vital. If you are considering permitting AI use in published scholarship—or incorporating AI tools at any point in your pipeline—here are the pros and cons that we see for a number of potential use cases:
3.1 Alt-Text Creation
- Pros of AI use:
- Allows quick and affordable compliance with accessibility requirements.
- Particularly appealing for retroactively making archival content more accessible, which might be time – or cost-prohibitive to do manually (either due to the amount of work, or the difficulty of reaching authors years later).
- Cons of AI use:
- Authors know their work the best and can most accurately describe the content.
- AI may misinterpret images and describe them incorrectly (e.g., hallucinations or biases).
- Even if technically accurate, AI descriptions will not have context and may not focus on the relevant/important elements of the image as a result.
3.2 Bibliography Creation/Formatting
- Pros of AI use:
- Theoretically makes it easier to follow the correct format for various citation styles.
- Cons of AI use:
- AI may fabricate new nonexistent references, or alter/combine/remove references that are already listed.
- Any errors in the bibliography make the rest of the paper appear suspect.
- AI may mis-identify individual elements within each reference, so there’s no guarantee that citation styles are being followed correctly.
3.3 Image Creation
- Pros of AI use:
- A quick and affordable method for adding non-substantive, non-scholarly visual interest to a journal (e.g., article thumbnails, cover illustrations).
- Cons of AI use:
- May reflect poorly on the journal’s professionalism: many readers can visually identify AI images, and may perceive them as low-quality and low-effort (i.e., public backlash to “AI slop”).
- Takes away jobs from artists and graphic designers.
- Often trained on artists’ work without their knowledge or consent, raising serious ethical concerns about the unauthorised use and potential plagiarism of creative work.
3.4 Keyword & Abstract Creation
- Pros of AI use:
- Probably the least-problematic potential use of AI in publishing, as keywords/abstracts are not per se part of the formal scholarship within the article.
- AI may be less likely to introduce errors when distilling an article to a lower level of specificity (e.g., summarising for an abstract or identifying frequently used keywords) as opposed to higher specificity (e.g., applying citation styles).
- Takes some pressure off of authors.
- Cons of AI use:
- Authors know their work the best and can most accurately describe the content.
- Confabulations and/or misinterpretations are still possible.
3.5 Language Translation
- Pros of AI use:
- Makes article content more accessible to a wider variety of people by allowing it to be read in multiple languages.
- Bypasses the language barrier for authors who may have otherwise faced obstacles to their work being published in their desired journals.
- Cons of AI use:
- Takes jobs away from skilled translators.
- Inaccuracies may be introduced during translation, due to either AI fabrications/biases or “lost in translation” misunderstandings.
- Any AI translation must undergo copyedit reviews by a human fluent in the destination language.
- May not be worth the effort, as readers can just do their own machine translation if they want (e.g., Google Translate).
4. Additional Resources
- Library Publishing Coalition (LPC) “AI Editorial Policy Guide”:
- International Association of Scientific, Technical & Medical Publishers (STM) “Ethical and Practical Guidelines for the Use of Generative AI in the Publication Process”:
- STM classification system for types of AI use:
- Committee on Publication Ethics (COPE) AI guidelines:
- Open Library of the Humanities (OLH) AI policy:
- Examples of AI policies from academic publishers:
- Examples of AI policies from commercial publishers:
