POLICIES

The Journal of Interdisciplinary AI Applications regards scholarly publishing as a process built on trust, responsibility, transparency, and academic integrity. Publication ethics is therefore treated not as a secondary issue, but as a foundational element of all editorial and peer review processes.

All manuscripts submitted to, reviewed by, and published in the journal are handled in accordance with principles of originality, accuracy, transparency, accountability, proper acknowledgment of sources, respect for ethical approvals, and scientific honesty. Authors, reviewers, and editors are each expected to understand and uphold their responsibilities within this ethical framework.

Originality and Proper Attribution

All submitted work must be original. Ideas, text, data, tables, figures, images, or other materials taken from other sources must be properly cited and acknowledged. Plagiarism, concealed plagiarism, translation plagiarism, excessive textual overlap, inappropriate reuse of prior work, and unattributed borrowing are unacceptable.

The journal may use similarity checks where necessary, but similarity percentages alone do not determine ethical acceptability. What matters most is the nature and context of overlap. Inappropriate textual or conceptual reuse outside standard technical phrasing may be treated as an ethical concern.

Duplicate Submission and Redundant Publication

A manuscript must not be under consideration by more than one journal at the same time. A work that has already been published, accepted elsewhere, or submitted simultaneously to another journal may not be presented as a new and original submission.

Fragmented publication, duplicate publication in another language without proper disclosure, and repeated publication of substantially overlapping findings from the same dataset may also raise ethical concerns and will be subject to editorial review.

Research Integrity and Data Honesty

Authors are expected to present research data honestly and accurately. Fabrication, falsification, selective reporting, omission of material findings, or misrepresentation of analytical procedures are serious ethical violations.

In fields such as artificial intelligence, data science, and analytical modeling, ethical concerns may also arise when performance is presented in a misleading manner, data leakage is concealed, validation procedures are inadequately explained, or methodological limitations are deliberately obscured.

Ethical Authorship

The list of authors must accurately reflect real scholarly contribution. Adding individuals who have not contributed meaningfully, excluding those who have contributed substantially, honorary authorship, gift authorship, and ghost authorship are not acceptable.

All authors should have reviewed and approved the final manuscript and should accept shared responsibility for the integrity of the work. Authorship disputes should preferably be resolved among the authors before submission; however, where ethical concerns arise, editorial intervention may become necessary.

Ethics Approval and Participant Rights

Research involving human participants, sensitive personal data, patient data, field interventions, experiments, interviews, surveys, observations, or similar ethically regulated procedures must have received the necessary ethics approval. Participant rights, privacy, informed consent, and data confidentiality must be protected.

Submissions lacking required ethics approval, or raising serious concerns regarding participant protection, may be excluded from review. The same principle applies to animal studies and other research subject to legal or ethical oversight.

Conflict of Interest and Transparency

Authors, reviewers, and editors must disclose any financial, institutional, commercial, personal, or academic relationships that could affect the objectivity of the work or its evaluation. Financial support, consultancy roles, company ownership, patents, institutional ties, or close personal relations may all be relevant.

The existence of a conflict of interest does not necessarily invalidate the research. However, failure to disclose such relationships is considered a serious ethical concern. The journal treats disclosure as an essential part of responsible scholarly communication.

Reviewer Ethics

Reviewers must treat manuscripts as confidential documents. They may not share, copy, use, or exploit submitted work for personal advantage. Reviews should be objective, constructive, respectful, and academically justified.

Core ethical expectations for reviewers include:

• confidentiality,
• impartiality,
• avoidance of conflicts of interest,
• academic courtesy,
• timely review,
• reasoned judgment.

Editor Ethics

Editors must evaluate manuscripts on the basis of scholarly quality and relevance to the journal’s scope, not on the basis of the author’s institution, nationality, academic title, gender, personal views, or professional standing. Where a conflict of interest exists, editors should recuse themselves and, where appropriate, reassign the manuscript.

Editorial decisions must be transparent, consistent, and grounded in academic quality. Editors have the authority to investigate suspected ethical breaches and, where necessary, initiate corrections, editorial notices, retractions, or rejection.

Ethical Use of Artificial Intelligence and Automated Tools

The journal recognizes the growing presence of artificial intelligence tools in academic workflows. However, such tools must be used ethically and transparently. AI systems cannot be listed as authors. The ultimate responsibility for AI-assisted content, analysis, references, visuals, or text remains with the human authors.

Any fabricated references, false summaries, misleading visuals, or unverifiable content generated through AI tools may be treated as ethical misconduct. The journal’s separate AI Use Policy provides further guidance and is considered a binding point of reference in this area.

Corrections, Retractions, and Editorial Responses

If a published article is later found to contain a serious error, ethical breach, major data problem, plagiarism, duplicate publication, or another issue affecting its reliability, the journal may take one or more of the following actions:

• publish a correction,
• issue an editorial note,
• publish an expression of concern,
• retract the article.

The appropriate response will depend on the nature, seriousness, and impact of the issue on the scholarly record. The aim is not punishment, but the preservation of accuracy and trustworthiness in academic publishing.

Complaints and Appeals

Authors, reviewers, or other relevant parties may submit reasoned complaints or appeals regarding editorial decisions, ethical concerns, or review processes. Such matters will be considered carefully and impartially. Additional editorial review may be undertaken where necessary.

However, personal accusations, unsupported allegations, or attempts to pressure the editorial process are not acceptable.

The Journal of Interdisciplinary AI Applications is committed to protecting originality, proper attribution, and academic integrity. All manuscripts submitted to the journal must be the original work of the author(s) and must properly acknowledge the ideas, words, data, figures, methods, and contributions of others.

Plagiarism in any form is unacceptable. This includes, but is not limited to:

• direct copying of text without proper quotation or citation,
• close paraphrasing without attribution,
• unattributed use of ideas, structure, or argumentation,
• translation plagiarism,
• plagiarism of figures, tables, images, or datasets,
• concealed reuse of previously published material,
• inappropriate self-plagiarism or redundant reuse of one’s own text.

The journal may use similarity-checking tools as part of editorial screening or ethical review. However, similarity percentages alone are not treated as the sole basis for decision-making. The editorial team evaluates the context, extent, location, and nature of overlap. Standard methodological phrasing or limited overlap in unavoidable technical expressions may not be treated in the same way as unattributed conceptual or textual copying.

Where plagiarism or ethically inappropriate textual overlap is suspected, the journal may:

• request clarification from the author(s),
• request revision and proper citation,
• reject the manuscript,
• suspend editorial processing,
• initiate a post-publication correction or retraction if the issue is discovered after publication.

If plagiarism is detected after publication, the journal reserves the right to publish a correction, editorial notice, or retraction, depending on the severity and scholarly impact of the issue.

Authors are responsible for ensuring that:

• all borrowed material is properly cited,
• verbatim quotations are clearly indicated,
• all references are accurate and complete,
• their submission does not contain any misleading or hidden reuse of prior work.

The journal treats plagiarism not only as a technical similarity issue, but as a breach of academic trust. The fundamental expectation is that all submissions reflect honest scholarship, transparent citation practice, and respect for intellectual contribution.

The Journal of Interdisciplinary AI Applications adopts an open-access publishing model. All content published in the journal is freely and immediately accessible to readers without subscription charges, paywalls, or institutional access restrictions. The journal supports the broad dissemination of scholarly knowledge and aims to make research available to the widest possible academic and societal audience.

Open access in this journal means more than free reading. It also requires clear communication regarding copyright, reuse rights, attribution requirements, and licensing conditions. For this reason, the journal states its copyright and licensing framework openly and transparently.

The intellectual and scientific responsibility for published articles belongs to the authors. By submitting a manuscript and agreeing to publication, authors grant the journal the right of first publication. The precise copyright arrangement and licensing structure are determined according to the journal’s adopted open-access framework.

The journal will clearly indicate the license applied to each published article. Once the final licensing model is confirmed, the journal will specify:

• whether redistribution is permitted,
• whether commercial use is allowed,
• whether derivative works are allowed,
• what type of attribution is required,
• how author rights are protected.

Authors remain responsible for obtaining all necessary permissions for third-party materials included in their manuscripts, including figures, tables, maps, images, scales, screenshots, datasets, and other copyrighted materials. The journal does not accept responsibility for materials used without proper authorization.

Under the journal’s open-access model, readers may access and use content in accordance with the applicable license terms and with proper attribution to the original work. The journal supports the lawful, ethical, and visible circulation of scholarly knowledge.

The journal’s objective is to protect both the author’s scholarly contribution and the openness of scientific communication. The final licensing details may be updated as the journal’s publishing framework develops, and the active license will always be displayed on each article page.

The Journal of Interdisciplinary AI Applications recognizes that artificial intelligence tools are increasingly used in academic writing, editing, data processing, and research support. The journal does not adopt an absolute prohibition on such tools, nor does it accept unrestricted or undisclosed use. Instead, it follows a balanced policy grounded in transparency, accountability, accuracy, and academic integrity.

Authors may use AI-assisted tools during the preparation of their work. However, the use of such tools does not transfer responsibility from human authors to the technology. Authors remain fully responsible for all content included in a submitted manuscript, including text, interpretations, references, data descriptions, visuals, and conclusions.

Core Principles

1. AI tools cannot be authors

No language model, generative system, automated software, or AI-based platform may be listed as an author. Authorship requires responsibility, accountability, and intellectual ownership, all of which rest with human contributors.

2. Meaningful AI use should be disclosed

If AI tools have been used in a meaningful way in drafting, summarizing, editing, content generation, table or figure preparation, or analytical support, this should be disclosed transparently in the manuscript or in a relevant declaration.

Where possible, the disclosure should indicate:

• the name of the tool,
• the purpose of use,
• the scope of its contribution,
• and the fact that final review and responsibility remain with the authors.

3. Authors are responsible for verification

AI tools may generate fabricated references, inaccurate summaries, misleading analyses, biased wording, or false factual claims. Authors are expected to review, verify, and correct any AI-assisted content before submission. Unverified insertion of AI outputs into a manuscript is not acceptable.

4. Fabricated or unverifiable content is prohibited

Any fake citation, invented quotation, non-existent dataset, false methodological statement, or misleading content resulting from AI use will be regarded as a serious ethical concern.

5. Limited language support may be treated differently

Minor use of AI for spelling correction, grammar improvement, readability enhancement, or translation support may be treated differently from direct content generation. However, where uncertainty exists, the journal encourages transparent disclosure.

6. Sensitive or confidential data must not be entered into third-party AI systems

Authors must not upload confidential, personal, patient-related, unpublished, copyrighted, or otherwise protected material into external AI systems in ways that may compromise privacy, legal responsibility, or data security.

7. AI-generated visuals are subject to scrutiny

Generative visuals must not be presented as if they were actual empirical results, scientific images, or authentic data outputs. If such materials are used in a manuscript, their nature and generation process should be made clear where relevant.

Example Disclosure

An example disclosure may be phrased as follows:

The journal does not automatically prohibit AI-assisted work. However, it does not accept AI use that is hidden, misleading, insufficiently verified, or inconsistent with academic integrity. The core expectation is that technological assistance must be managed responsibly and transparently.

The Journal of Interdisciplinary AI Applications supports transparency, verifiability, and reproducibility in scientific research. In fields such as artificial intelligence, data science, analytics, and intelligent systems, the journal recognizes that openness regarding data, code, methodological detail, and analytical procedures contributes significantly to scientific reliability.

Authors are encouraged, where possible and appropriate, to share the datasets, code, model structures, parameter settings, preprocessing procedures, evaluation criteria, and other materials necessary to support the reproducibility of their work. However, the journal also acknowledges that full data sharing is not possible in every study.

Data sharing may be limited in cases involving:

• personal data,
• sensitive health or patient-related information,
• institutional confidentiality agreements,
• contractual or commercial restrictions,
• legal limitations,
• materials whose disclosure could create ethical or security risks.

Where full sharing is not possible, authors are expected to explain the reason clearly and honestly. In such cases, alternative forms of transparency are encouraged, including:

• anonymized datasets,
• synthetic or sample data,
• summarized data presentation,
• code sharing,
• repository links where available,
• detailed methodological explanation,
• controlled or request-based access.

Even when data cannot be shared openly, the methods used in the study should be described in sufficient detail to allow readers to understand how the research was conducted, how the data were handled, how analyses were performed, and how conclusions were reached.

Where appropriate, authors are encouraged to state:

• the source of the data,
• the availability status of the data,
• whether the data are openly accessible, restricted, or available upon request,
• whether code or scripts are available,
• any technical details necessary for reproducibility,
• the reason for any sharing limitation.

The journal does not treat data sharing as a rigid one-size-fits-all condition. Rather, it views it as a responsible scholarly practice to be assessed in light of disciplinary norms, legal obligations, ethical constraints, and the nature of the research. The central objective is to ensure that research is presented in a manner that is as transparent, understandable, and verifiable as reasonably possible.

The Journal of Interdisciplinary AI Applications is committed to preserving the accuracy, integrity, and reliability of the scholarly record. When significant problems are identified in a manuscript after publication, the journal may take corrective action in a manner proportionate to the seriousness and nature of the issue.

Post-publication issues may include, but are not limited to:

• significant factual or analytical errors,
• ethical concerns,
• plagiarism or inappropriate overlap,
• unreliable data or findings,
• duplicate publication,
• undeclared conflicts of interest,
• authorship disputes affecting the integrity of the record.

Depending on the case, the journal may take one or more of the following actions:

Correction

A correction may be published where the article remains valid overall, but an error requires public clarification or amendment. Corrections may relate to author details, references, figure labels, methodological explanations, or other issues that do not invalidate the central findings of the work.

Editorial Note or Expression of Concern

Where a serious concern is under investigation, but the available evidence is not yet sufficient for retraction or definitive correction, the journal may issue an editorial note or an expression of concern. This serves to inform readers that questions have been raised regarding the article.

Retraction

A retraction may be issued where the article is found to be seriously unreliable, ethically compromised, plagiarized, fraudulently produced, substantially duplicated, or otherwise unsuitable to remain part of the formal scholarly record. Retraction is a corrective measure intended to preserve academic trust, not a punitive act.

The journal will assess each case individually and may seek clarification from the author(s), reviewers, institutions, or other relevant parties where necessary. Editorial action will be based on fairness, proportionality, and the need to protect the reliability of published scholarship.

Where a correction, editorial note, or retraction is issued, it will be linked clearly to the original article so that the scholarly record remains transparent and traceable.