Generative AI Policy

Journal of Accounting and Management (JAM)  |  e-ISSN 3048-345X

1. Scope and Definitions

This policy covers generative AI, large language models, image and code generators, transcription and translation systems with generative functions, and similar tools used in research, manuscript preparation, peer review, editorial work, production, or post-publication communication. Ordinary spelling and formatting tools that do not generate substantive intellectual content are distinguished from material GenAI use.

2. AI Cannot Be an Author

An AI system cannot meet authorship requirements because it cannot consent, hold copyright, disclose conflicts, verify evidence, or accept responsibility. AI tools must not be listed as authors, co-authors, or contributors. Human authors remain fully accountable for every claim, citation, dataset, image, code element, and disclosure.

3. Permitted Author Uses

• Language polishing, grammar, readability, or formatting assistance that does not introduce substantive ideas may be used with human verification.
• Substantive assistance with text, analysis, code, figures, translation, synthesis, study design, or interpretation must be disclosed.
• AI may not fabricate data, participants, quotations, references, images, results, ethics approval, consent, or author contributions.
• Authors must verify accuracy, originality, citations, permissions, privacy, bias, and reproducibility and retain relevant prompts and outputs when material to verification.
• Confidential, personal, proprietary, embargoed, or participant-identifiable data may not be entered into a tool without lawful authority, safeguards, and required consent.

4. Required Disclosure

Material use must be described in the Methods, Acknowledgments, or a dedicated “Generative AI Use” statement. The disclosure should identify the tool, provider, model/version, access date, purpose, affected manuscript components, human verification, and—when methodologically relevant—prompts, parameters, and output handling.

5. Data, Images, Code, and References

• AI-generated or altered images must be identified and must not misrepresent evidence. Manipulation of research images or documents is prohibited.
• AI-assisted analysis or code must be described sufficiently for evaluation and reproducibility; validation against source data is required.
• References suggested by AI must be checked against the original publication and DOI; nonexistent or inaccurate citations are the authors’ responsibility.
• Synthetic data must be clearly labeled, justified, generated under a reproducible method, and never presented as observed human or organizational data.

6. Reviewers and Editors

• Unpublished manuscripts, reports, correspondence, and personal data must not be uploaded to public or unapproved AI systems.
• Reviewers may not delegate assessment or generate a review report with an unauthorized AI tool.
• Editors may use approved secure tools for administrative support only when confidentiality, data retention, intellectual property, security, and human oversight are assured.
• AI output may inform checks but cannot select reviewers, make final judgments, accept or reject a manuscript, or determine misconduct.
• Any material approved AI assistance in editorial work must be documented internally and disclosed when necessary for transparency.

7. Assessment and Consequences

Undisclosed or inappropriate AI use is assessed according to materiality, intent, reliability, privacy impact, and effect on the record. JAM may request files or clarification, require disclosure or revision, reject a manuscript, refer serious concerns, or publish a correction, Expression of Concern, or retraction under the applicable policy. A similarity or AI-detection score alone is not proof and will not determine the outcome.

8. Policy Review and Relationships

JAM reviews this policy at least annually or sooner when technology, law, or recognized scholarly guidance changes.