Publications Ethics

KEUNIS upholds rigorous publication ethics to maintain the integrity and quality of its scholarly communications. These standards are informed by the Committee on Publication Ethics (COPE) guidelines.​

Duties of Authors:

• Reporting Standards: Authors are expected to present accurate and honest accounts of their research, including objective discussions of their findings' significance. Fabrication, falsification, or inappropriate data manipulation are considered unethical. Manuscripts should provide sufficient detail and references to allow replication of the study. 

• Originality and Plagiarism: Submissions must be original works not under consideration elsewhere. Plagiarism, including self-plagiarism without proper citation, is strictly prohibited. Authors should ensure appropriate acknowledgment of sources and use plagiarism detection tools, such as Turnitin, to verify originality. ​

• Multiple, Redundant, or Concurrent Publication: Authors should not submit manuscripts describing essentially the same research to more than one journal simultaneously. Such actions are considered unethical. ​

• Acknowledgment of Sources: All contributors who do not meet the criteria for authorship should be acknowledged, specifying their contributions. Funding sources must also be disclosed.

Duties of Editors:

• Publication Decisions: Editors are responsible for deciding which articles to publish, guided by the journal's editorial policies, legal considerations, and the potential impact on the readership.

• Fair Play: Manuscripts are evaluated based on intellectual content without discrimination regarding race, gender, sexual orientation, religious belief, ethnic origin, citizenship, or political philosophy. ​

• Confidentiality: Editors and editorial staff must maintain the confidentiality of submitted manuscripts and not disclose information about them to unauthorized parties. ​

• Disclosure and Conflicts of Interest: Editors should recuse themselves from decisions where they have conflicts of interest, ensuring transparency and impartiality in the editorial process. ​

Duties of Reviewers:

• Confidentiality: Reviewers must treat manuscripts as confidential documents, not sharing or discussing them outside the review process. ​

• Standards of Objectivity: Reviews should be conducted objectively, with clear arguments supporting the reviewer's views. Personal criticism of the authors is inappropriate. 

• Acknowledgment of Sources: Reviewers should identify relevant published work that has not been cited by the authors and notify the editor of any substantial similarities with other published works. ​

• Disclosure and Conflicts of Interest: Reviewers must disclose any conflicts of interest and recuse themselves from the review process if such conflicts exist. ​

By adhering to these ethical guidelines, KEUNIS strives to uphold the highest standards in scholarly publishing, fostering trust and integrity within the academic community.

 

Artificial Intelligence (AI)

This policy explains how our authors, editorial teams, and peer reviewers can use artificial intelligence (AI) when preparing or reviewing work for KEUNIS. Further details are provided below on the specific use-cases for this technology; any questions should be directed to the appropriate KEUNIS editorial contact.

Copywriting (creating, drafting, or writing) any part of a submission using generative AI tools and technology to generate new material is not permitted.

Copy-editing (correcting, editing, formatting, modifying, or refining) all or part of an author’s own original existing work using generative AI tools and technology to improve its structure and the clarity of the language and grammar is permitted, ensuring users adhere to the following overarching principles.

KEUNIS’s overarching principles of AI usage

• Authors and peer reviewers are responsible and accountable for the accuracy and integrity of their work.
• AI tools and technology must be used responsibly and transparently.
• AI tools and technology should not replace human involvement in the publication process but instead supplement it.

KEUNIS recognises the growing role of AI tools and technology in the creation of academic content and that authors may wish to use these when preparing work to be submitted. Productive and responsible use of this technology stems from human oversight, authorship, creativity, and expertise enhanced by AI capabilities, so that the content produced remains original and aligned with professional ethical and publishing principles. The harnessing of these tools to support the development of academic content should abide by privacy, confidentiality, and compliance obligations (including data protection laws and intellectual property rights), preserve editorial standards, adhere to copyright and licensing requirements, and foster transparency with readers. Authors must ensure they remain fully accountable for any sources used and the work produced.

Declaring AI Usage

Any use of AI tools, including Large Language Models (LLMs), for the creation, development, or generation of an KEUNIS publication must be flagged, clearly and transparently, by authors within the Methods and Acknowledgements (or another appropriate section) of the work. Authors must describe the content created or modified as well as appropriately cite the name and version of the AI tool used; any additional works drawn on by the AI tool should also be appropriately cited and referenced, with any necessary rights for reproduction secured. If not provided, this information may be requested during the submission and peer review process, or after publication, and its absence could result in the rejection of the work or post-publication action. Authors must abide by KEUNIS’s principles of generative AI usage and should review the terms and conditions or terms of use associated with the AI tool employed to ensure compliance.

Authors must take full responsibility for the accuracy of all content considered for publication and verify that the material submitted, including citations and references, is correct, appropriately correlates with the research, and aligns with KEUNIS’s research and publishing ethics. Standard tools that are used to improve spelling and grammar are not included within the parameters of this guidance, specifically those tools not using generative AI. KEUNIS reserves the right to determine whether the use of an AI tool is permitted in a submitted work, and the right to reject submissions and to take appropriate post-publication action on published material found to feature fabricated or fraudulent AI-generated content.

Generative AI usage key principles

To harness the benefits of AI tools and technology in an accountable, responsible, and transparent manner when preparing work for submission, KEUNIS emphasises the following key principles of generative AI usage for authors to ensure appropriate human oversight of the writing process and adherence to KEUNIS’s contractual warranties, including that their material is original, not previously published, and permission has been cleared for any third-party material included.

AI and authorship

In accordance with COPE’s position statement on AI tools, LLMs cannot be credited with authorship as they do not have legal standing or the ability to assign copyright, are incapable of conceptualising a research design without human direction, and cannot be accountable for the integrity, originality, and validity of the published work.

AI and content creation

• Copywriting any part of a submission using a generative AI tool/LLM is not permitted, including the generation of the abstract or the literature review. In line with standard academic practice, however, KEUNIS permits the use of examples of generative AI for illustrative purposes as part of scholarly critique and discussion; these examples must be appropriately flagged in the text and be fully cited and referenced in accordance with formatting requirements.
• Generating, manipulating, or reporting research data and results using a generative AI tool/LLM is not permitted.
• In-text reporting of statistics using a generative AI tool/LLM is not permitted due to concerns over the authenticity, integrity, and validity of the data produced, although the use of such a tool to aid in the analysis of the work is permissible with appropriate and transparent declaration.
• The submission and publication of images created by AI tools or large-scale generative models is subject to their intended purpose and potential rights requirements. Purpose is here defined as either illustrative, which is permitted, or factual/evidential, which is not permitted. Any such usage must not breach KEUNIS’s plagiarism policy. The following types of AI-generated images are permitted for illustrative or visualisation purposes only, but we encourage authors to check this in advance of submission with the appropriate editorial contact: explanatory diagrams, graphical abstracts, teaching illustrations, conceptual visualisations, and process flow diagrams.
  • These images must be accurate, free from errors, and not misrepresent information; they should be clearly labelled as generated by AI in accordance with KEUNIS’s attribution policy and cite the name and version of the tool used. The images should also have a clear scientific purpose, and must be logically coherent, reflect the data or research correctly, and be based on factual truths.
  • Cover art created by generative AI is, however, not permitted.
  • AI-generated factual or evidential images used to support specific scientific or technical claims without any basis in actual research are not permitted.
  • Any such third-party images must be clearly and transparently labelled as to their origin and meet our permissions and licensing specifications.
  • Any modifications made to images or figures using generative AI tools and technology must not contravene KEUNIS’s policy on image and figure manipulation.

AI and content editing

Copy-editing a submission or a peer review report using a generative AI tool/LLM to improve its language and readability is permitted as this mirrors standard tools already employed to improve spelling and grammar, and uses existing author-created material, rather than generating wholly new content, while the author(s) remains responsible for the original work. Authors and peer reviewers should be conscious of the potential for bias, fabrication, misinformation, inaccurate attribution, and plagiarism when using such tools, and should therefore verify the work prior to submission. Authors should maintain and provide documentation of all AI technology used for this purpose, and it should not be employed to replicate the unique work of others.

KEUNIS defines copy-editing as modifying existing material created by the author to improve its language, grammar, and spelling, whereas copywriting would be the creation of new material; this echoes the guidance provided by STM’s Recommendations for a Classification of AI Use in Academic Manuscript Preparation.

Authors and peer reviewers are responsible and accountable for the work that is submitted to KEUNIS and take full responsibility for the accuracy and integrity of any AI usage.

AI evaluation and peer review

KEUNIS operates the following key principles when it comes to the use of artificial intelligence (AI) by our editorial teams and peer reviewers:

1. Any work or files submitted to KEUNIS for consideration and review should not be uploaded to a generative AI tool or Large Language Model (LLM).
2. Reviewers may, however, use a generative AI tool to copy-edit and improve the quality of the language in their review; in such cases, they maintain responsibility for the accuracy and integrity of the review, and must declare this usage transparently to the editorial team.

Any work submitted to KEUNIS for consideration and review should be treated as confidential, meaning that sharing this material with another person or uploading it to a generative AI tool or LLM for assessment or evaluation would violate the author’s confidentiality, as well as any proprietary and/or data privacy rights.

There are additional concerns regarding the use of generative AI tools for peer review due to biases in the datasets of these models and the reliability of their ability to assess content, with the risk of generating false, flawed, or inaccurate results. To maintain trust in the integrity of the published record, KEUNIS does not permit the use of generative AI tools or LLMs to assist in the review, evaluation, or decision-making process of any part of an article, case study, or chapter by either a member of a journal’s editorial team or a reviewer, in accordance with KEUNIS’s principles of peer review. Any files under review should not be uploaded to a generative AI tool or LLM.

KEUNIS does, however, permit the use of generative AI tools for the copy-editing only of peer review reports to improve the quality of the language; the reviewer remains responsible for the accuracy and the integrity of the review, and must declare such usage clearly and transparently.

Peer reviewers are ultimately responsible for the reviews they provide and accountable for their accuracy, rigour, and validity, which, as per COPE’s position statement on AI tools, cannot be replicated by a non-human generative AI. Any breach of the integrity or trust of the review process as described above will be perceived as peer review misconduct.

AI usage table

The following table should be used as a reference point for submissions; this is not an exhaustive list of AI usage or applications to academic work. The use-cases are based on STM’s Recommendations for a Classification of AI Use in Academic Manuscript Preparation. Please refer to the overarching principles above for further guidance on this matter. Any questions on specific use-cases that are not addressed below should be directed to the appropriate KEUNIS editorial contact.

AI Usage	Example	Permitted?
Abstract creation	Creating, drafting, or writing all or part of the abstract using author-inputted prompts into a generative AI tool/LLM; this includes using AI tools to expand on text or generate machine summaries of previous work.	NO
Abstract copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the abstract to improve clarity of language and grammar.	YES
Hypothesis creation	Creating, drafting, or writing all or part of the hypothesis or a set of research questions through author-inputted prompts into a generative AI tool/LLM.	NO
Introduction creation	Creating, drafting, or writing all or part of the introduction through author-inputted prompts into a generative AI tool/LLM; this includes using AI tools to expand on text or generate machine summaries of previous work.	NO
Introduction copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the introduction to improve clarity of language and grammar.	YES
Methodology ideation	Generating methodological approaches or identifying viable models for the initial research proposal by using a generative AI tool/LLM in the manner of a traditional search engine or study.	YES
Methodology creation	Creating, drafting, or writing all or part of the methodology through author-inputted prompts into a generative AI tool/LLM; this includes using AI tools to expand on text or generate machine summaries of previous work.	NO
Methodology copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the methodology to improve clarity of language and grammar.	YES
Literature review/bibliography ideation	Generating sources of relevant reading or identifying gaps in the literature for the initial research proposal by using a generative AI tool/LLM in the manner of a traditional search engine or study to assist in the compilation of a reference list.	YES
Literature review/bibliography creation	Creating, drafting, or writing all or part of a list of citations/references for the literature review, or evaluating and analysing the literature through author-inputted prompts into a generative AI tool/LLM.	NO
Literature review/bibliography copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the literature review/bibliography to improve clarity of language and grammar.	YES
Data generation	Creating or generating research data and results through author-inputted prompts into a generative AI tool/LLM.	NO
Data visualisation	Generating figures/tables/infographics to provide a visual representation of results based on the author’s own already analysed and existing data through author-inputted prompts into a generative AI tool/LLM in the manner of traditional data visualisation.	YES
Results analysis	Analysing or describing the data/results through author-inputted prompts into a generative AI tool/LLM.	NO
Results summary	Summarising the author’s original existing data/results using a generative AI tool/LLM to improve accessibility and data curation.	YES
Analysis/discussion copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the analysis/discussion to improve clarity of language and grammar.	YES
Conclusion creation	Creating, drafting, or writing all or part of the conclusion through author-inputted prompts into a generative AI tool/LLM.	NO
Conclusion copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the conclusion to improve clarity of language and grammar.	YES
Code creation	Generating code for the research through author-inputted prompts into a generative AI tool/LLM alone without any other human involvement.	NO
Code copy-editing	Improving the author’s original existing work using a generative AI tool/LLM by either refining, correcting, editing, or formatting the code to improve its readability.	YES
Cover art/evidential or factual images	Creating an image through author-inputted prompts into a generative AI tool such as (but not limited to) DALL-E; specifically, the use of text-to-image models to generate evidential or factual images from language descriptions for commercial use or to support scientific or technical claims without any basis in the actual research or real data.	NO
Explanatory diagrams/teaching illustrations/conceptual visualisations/process flow diagrams/graphical abstracts	Creating accurate representations of information or visualisations of the data or research through the use of a generative AI tool whereby the data can be attributed, checked, and verified for accuracy.	YES
Conceptual and methodological figure generation	Generating, refining, correcting, editing, or formatting a figure/table/infographic to represent a theoretical concept of the research visually or to present a visual representation of a methodology based on the author’s existing conceptual framework/methodological approach through author-inputted prompts into a generative AI tool/LLM.	YES
Translation	Translating the author’s original existing and previously unpublished work into English using a generative AI tool/LLM (the author must declare such usage and be able to confirm the accuracy and integrity of the work, remaining accountable and responsible for the content submitted).	YES
Presenting AI-generated content as though it were original research data/results from non-machine sources	Using generative AI tools or technology to create data, text, images, graphs, spectra, or other content that are presented as though it were original research data/results collected or analysed from other, non-machine sources.	NO

KEUNIS’s use of AI tools and technology

As a publisher committed to accountability and transparency, KEUNIS recognises that there is a place for the ethical and responsible use of AI tools and technology that supports editorial and publishing workflows, without detrimental impact to the core processes underpinning them or affecting the integrity and quality of the research content itself. 

As a publisher, we have a moral obligation to preserve trust in our processes and publications for our authors, customers, and readers. Any use of AI tools and technology by KEUNIS will therefore be transparently disclosed as appropriate and undertaken in accordance with any relevant data privacy and protection laws and requirements. Our use of AI tools and technology will also take into consideration the potential for structural biases and environmental and societal ramifications, and we will work to mitigate any negative effects to people and the planet. 

KEUNIS maintains human oversight of its AI tools and technology, including any outcomes and results generated; no decision is made based on an algorithm alone to prevent perpetuating real-world biases and inequities. 

We acknowledge that this area will continue to evolve at pace within the sector, and we will monitor these developments to ensure that our own usage, alongside our guidance and policies, continues to reflect industry best practice standards so that our publications remain committed to high-quality, trusted academic content, and that our practices and workflows remain transparent for our authors, customers, and readers.