Publication Ethics & Policies

Artificial Intelligence and Machine Learning Review (AIMLR) is committed to maintaining rigorous ethical standards and responsible publishing practices in all aspects of scholarly communication. The journal enforces comprehensive guidelines to ensure that all published research demonstrates originality, accuracy, transparency, and integrity. Authors, reviewers, and editors are expected to uphold these principles throughout the research, submission, review, and publication process.

Research Integrity

All manuscripts submitted to AIMLR must present original research that has not been published elsewhere and is not under consideration by any other journal. Plagiarism, including self-plagiarism, is strictly prohibited. Authors are expected to report research findings honestly and with sufficient detail to enable reproducibility. All sources of funding, financial support, and potential conflicts of interest must be disclosed clearly to promote transparency and accountability.

Ethical Compliance in Research

Studies involving human participants or animals must comply with internationally recognized ethical standards and obtain approval from appropriate institutional review boards or ethics committees. Documentation of such approvals must be provided upon request. Participant confidentiality must be protected at all stages, and personally identifiable information may only be disclosed with explicit consent. Authors are responsible for ensuring that their research adheres to ethical requirements relevant to their field.

Authorship and Contribution

Authorship should be limited to individuals who have made substantial contributions to the conception, design, execution, or interpretation of the research. The order of authors must accurately reflect the relative contribution of each individual, and all listed authors must approve the final manuscript and its submission. Individuals who contributed to the work but do not meet authorship criteria should be acknowledged appropriately.

Peer Review Integrity

AIMLR operates a Double-Blind Peer Review system to ensure impartial evaluation. Reviewer identities remain confidential to authors, and reviewers must treat all manuscripts and associated data as strictly confidential. Reviews should be objective, constructive, and completed within the designated timeframe to support timely publication. Editors ensure that evaluation is based solely on scientific merit, novelty, and relevance.

Ethical Publication Practices

Manuscripts must consist of original content, properly attributed, and submitted exclusively to AIMLR. Any duplication of published work, plagiarism, or misrepresentation of data is strictly prohibited. Minor errors identified post-publication are corrected via corrigenda or errata. In cases of major ethical violations, fabrication, or significant inaccuracies, AIMLR reserves the right to retract the article, in consultation with the authors and relevant authorities.

Conflict of Interest

All authors, reviewers, and editors are required to disclose any financial, personal, or professional conflicts of interest that could influence research findings, manuscript evaluation, or editorial decisions. Transparency in such matters ensures the integrity of the publication process and maintains trust in the journal.

Editorial Independence and Decision-Making

Editorial decisions are guided exclusively by the scientific quality, originality, and relevance of the manuscript. Decisions must remain independent of external pressures, personal relationships, or financial considerations. Authors have the right to appeal editorial decisions by providing a reasoned justification, which will be reviewed impartially.

Open Access and Copyright

AIMLR follows a fully open-access model, ensuring that all published research is freely accessible to the global scientific community. Authors retain certain rights for non-commercial use, educational purposes, and citation, provided proper credit is given to AIMLR.

Data Transparency and Reproducibility

Authors are encouraged to share datasets, computational models, and methodological details to enhance transparency and reproducibility. Supplementary material, such as code or additional data, should be clearly cited and submitted alongside the manuscript whenever possible.

By adhering to these policies, AIMLR ensures that every published article maintains the highest standards of ethical conduct, scientific rigor, and academic integrity, reinforcing its commitment to advancing research in Artificial Intelligence, Machine Learning, and Data Science.