[AISWorld] Fwd: Special Issue on Responsible and Reproducible Machine Learning

[Ebrahim Bagheri]

*Call for Papers*
Responsible and Reproducible Machine Learning (Festschrift for Prof. Stan
Matwin)

*Submission deadline: Monday, 30 September 2024*
We are pleased to invite submissions for a special issue of *Computational
Intelligence* on “Responsible and Reproducible Machine Learning.” In the
era of rapid advancements in artificial intelligence and machine learning,
ensuring that these technologies are developed and deployed in a
responsible, transparent, and reproducible manner is of paramount
importance. This special issue aims to gather pioneering research that
addresses these critical dimensions, fostering the creation of machine
learning systems that are not only effective but also trustworthy and
ethically sound. We seek contributions that provide deep insights into the
theoretical underpinnings, innovative methodologies, and practical
applications of responsible and reproducible machine learning.
We welcome original research articles, comprehensive reviews, and case
studies that delve into the multifaceted aspects of this field. Our goal is
to create a platform for interdisciplinary dialogue and to highlight
innovative approaches that advance the principles of explainability,
transparency, ethical AI, and privacy-preserving analytics. Submissions
that explore novel frameworks, propose new models, or offer empirical
evaluations are particularly encouraged. By bringing together diverse
perspectives and cutting-edge research, this special issue aims to drive
forward the discourse on how to responsibly benefit from the power of
machine learning technologies.*Topics of interest:*

   - Methods and frameworks for ensuring explainability in machine learning
   models;
   - Techniques for enhancing transparency in machine learning processes;
   - Ethical considerations and guidelines for responsible AI development;
   - Approaches to privacy-preserving analytics in machine learning;
   - Case studies on the implementation of responsible and reproducible
   machine learning in various industries;
   - Cross-disciplinary approaches to integrating ethical principles in
   machine learning;
   - Evaluation metrics and benchmarks for reproducibility in machine
   learning research;
   - Impact of regulatory frameworks on the development and deployment of
   machine learning systems;
   - Algorithmic fairness and bias mitigation in machine learning models;
   - Verification and validation of machine learning systems for
   reproducibility;
   - Design and implementation of ethical AI frameworks and toolkits;
   - User-centric approaches to explainability and transparency in AI
   systems.

*Guest Editors (in alphabetical order):*
Dr. Ebrahim Bagheri <bagheri at torontomu.ca> (Lead)
Toronto Metropolitan University,
Canada
Dr. Marina Sokolova <sokolova at uottawa.ca>
University of Ottawa,
Canada
Dr. Sebastien Gambs <gambs.sebastien at uqam.ca>
University of Quebec,
Canada
Dr. Nathalie Japkiwocz <nathalie.japkowicz at american.edu>
American University,
USA
Dr. Amilcar Soares <amilcar.soares at lnu.se>
Linnaeus University,
Sweden*Editor-in-Chief:*
Diana Inkpen
University of Ottawa, Canada*Keywords:* Explainability; Transparency;
Ethical and Responsible AI; Privacy-Preserving Analytics; Causal
Inference.*Submission
guidelines/instructions:*
Please refer to the Author Guidelines
<https://onlinelibrary.wiley.com/page/journal/14678640/homepage/forauthors.html>
 to prepare your manuscript. When submitting your manuscript, please answer
the question: “Is this submission for a special issue?” by selecting the
special issue title from the drop-down list.*Important dates:*

   - Submission deadline: 30 September 2024
   - Initial notification: 15 November 2024
   - Revisions due: 15 December 2024
   - Notification: 15 January 2025

https://onlinelibrary.wiley.com/page/journal/14678640/homepage/call-for-papers/si-2024-000434