Operational Research for Fairness, Privacy and Interpretability in Machine Learning

Explore the intersection of fairness, privacy, and interpretability in machine learning through this 45-minute DS4DM Coffee Talk presented by Julien Ferry from LAAS-CNRS. Delve into the application of operational research and combinatorial optimization tools to develop responsible AI. Gain insights into Ferry's PhD research, which examines the interactions between these crucial aspects of ethical machine learning. Learn about the innovative use of Integer Linear Programming to create interpretable, fair, and optimal models. Discover how this approach can contribute to the advancement of responsible AI practices and the development of more ethical machine learning systems.