Ethical Guidelines

Artificial Intelligence and Machine Learning Review (AIMLR) is committed to upholding the highest ethical standards in AI and ML research publication. We emphasize values such as algorithmic transparency, reproducibility, fairness, accountability, and respect for human dignity in AI systems. Our ethical guidelines align with the principles set out by the Committee on Publication Ethics (COPE) and incorporate AI-specific considerations from leading AI ethics frameworks. By submitting a manuscript to AIMLR, authors agree to adhere to these ethical guidelines.

Authorship

Authorship is granted to individuals who have made substantial contributions to:

  • The conception, design, or architecture of AI/ML models and systems
  • The development, implementation, or coding of algorithms
  • Data collection, preprocessing, or curation
  • Experimental design, analysis, or interpretation of results
  • Drafting or critically revising the technical content
  • Approving the final version of the work
  • Agreeing to be accountable for all aspects of the work, including algorithmic performance and ethical implications

Individuals who provided datasets, computational resources, or technical support without meeting full authorship criteria should be acknowledged. The corresponding author is responsible for handling correspondence during the publication process and acts on behalf of all co-authors. They remain the primary point of contact for any post-publication inquiries about the AI systems described.

AI-Specific Ethical Considerations

Algorithmic Fairness and Bias: Authors must address potential biases in training data, model architecture, and evaluation metrics. Studies should include bias analysis and mitigation strategies where applicable.

Transparency and Explainability: Research involving complex AI systems should include appropriate explainability methods, model documentation, and transparency about limitations.

Reproducibility: Authors are expected to provide sufficient detail for reproducing AI/ML experiments, including hyperparameters, random seeds, and computational environment details.

Data and Model Provenance: Clear documentation of data sources, preprocessing steps, and model development processes is required.

Conflict of Interest

Authors must disclose any potential conflicts of interest—financial, non-financial, professional, or personal—that could affect the objectivity or integrity of the AI research. This includes relationships with companies whose products are evaluated, funding from AI technology vendors, or personal investments in AI-related enterprises.

Funding

Authors must provide details of all funding sources received during the preparation of the manuscript, including grants from AI research institutions, technology companies, or government AI initiatives, as required by funding and grant-awarding bodies.

Studies Involving Human Data or Subjects

Research involving human data, user studies, or human-AI interaction must comply with institutional and national ethical policies. Proper documentation, such as ethical committee approvals, informed consents for data collection, and data privacy protection measures must be included and described.

Ethical Oversight for AI Systems

Research involving AI systems with potential real-world impact (healthcare, autonomous systems, financial applications, etc.) must include risk assessment and ethical considerations. Studies using sensitive data must clearly state data protection protocols and compliance with relevant regulations (GDPR, HIPAA, etc.).

Data, Code, and Model Sharing

Authors are strongly encouraged to share data, code, pre-trained models, or computational notebooks related to their research to ensure reproducibility and reliability in AI/ML research. Data should be deposited in suitable repositories (GitHub, Zenodo, Hugging Face, etc.), and their locations must be described in the manuscript. When sharing is not possible due to privacy or commercial constraints, authors must explain the limitations.

Advertising

AIMLR allows limited and targeted advertising on its website to promote selected AI/ML research content published by the journal, with clear distinction from editorial content.

Complaints and Appeals

Concerns regarding potential violations of publication ethics in AI research should be directed to ethics@aimlr.org. All complaints and appeals will be handled rigorously in accordance with COPE guidelines, with special attention to AI-specific ethical considerations.

Corrections and Retractions

If errors are identified post-publication, AIMLR will publish a corrigendum or erratum. Retractions will be issued if errors significantly affect the AI model performance, results, or conclusions. Serious ethical malpractices, such as plagiarism, data fabrication, or unethical AI applications may result in retraction and notification to relevant academic bodies or institutions.

Malpractice and Plagiarism Policy

Authors must avoid research misconduct, including fabrication, falsification, duplication, and plagiarism. Suspected cases of malpractice in AI research will be investigated by editors following COPE best practices. If malpractice is confirmed, even post-publication, the article may be retracted, and authors may be banned from submitting additional manuscripts for a defined period.

Plagiarism in AI Research

Plagiarism involves using someone else's ideas, code, architectures, or data without proper acknowledgment. This includes unauthorized use of open-source code, model architectures, or research concepts without appropriate citation. All incoming manuscripts are screened for plagiarism using advanced similarity-checking tools. Manuscripts with high similarity to existing literature or code repositories may be rejected immediately.

AI-Generated Content Disclosure

Authors must disclose the use of AI-assisted technologies in the research process, including AI tools for writing, coding, or data analysis, while maintaining that human authors are responsible for the content's accuracy and integrity.

Responsibilities

Authors

  • Ensure the work is original and not under consideration elsewhere
  • Disclose any use of AI-assisted technologies in the research process
  • Provide proper documentation of datasets, algorithms, and experimental setups
  • Obtain permission for any copyrighted material, including code and datasets
  • Properly cite influential works of others in AI/ML literature
  • Approve the final version of the article and agree to its publication
  • Disclose any financial or other conflicts of interest related to AI technologies
  • Promptly notify editors of errors or inaccuracies in AI models or results, even post-publication

Reviewers

  • Provide objective evaluations of AI methodology, results, and ethical considerations
  • Maintain confidentiality of the submitted manuscript and any associated code/data
  • Respond within the suggested timeframe
  • Declare conflicts of interest with AI technologies or companies mentioned in the manuscript

Editors

  • Make objective and timely publication decisions based on AI research quality
  • Ensure fair consideration of diverse AI approaches and methodologies
  • Avoid discrimination based on race, gender, origin, institutional affiliation, etc.
  • Maintain confidentiality of submitted manuscripts and review materials
  • Uphold ethical standards specific to AI and machine learning research