Learning Fair Rule Lists
The widespread use of ML models in high stakes decision-making systems raises many ethical issues concerning fairness and interpretability. While the research in these domains is booming, very few works have addressed these two issues simultaneously. To solve this shortcoming, we propose FairCORELS, a supervised learning algorithm whose objective is to learn at the same time fair and interpretable models. FairCORELS is a multi-objective variant of CORELS, a branch-and-bound algorithm, designed to compute accurate and interpretable rule lists. We also made additional contributions regarding search strategies for improving the performances of FairCORELS.
I am a postdoctoral researcher at UQAM, working with Sébastien Gambs. My research interests are data privacy, optimization, and machine learning. I earned my Ph.D. in Computer Science at Université Toulouse III, under the supervision of Marie-José Huguet and Marc-Olivier Killijian. During my Ph.D. I was affiliated to LAAS-CNRS, a member of both ROC and TSF research groups, and have worked on privacy-enhancing technologies for ridesharing. Before that, I received my Engineer’s degree in Software Engineering from ENSA Khouribga.