CFP: Responsible and Reproducible Machine Learning (Festschrift for Prof. Stan Matwin)

[Marina Sokolova]

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

https://lnkd.in/gN_rRSY3

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):
Ebrahim Bagheri
Sebastien Gambs
Nathalie Japkowicz
Amílcar Soares
Marina Sokolova