NAVER LABS Europe seminars are open to the public. This seminar is virtual and requires registration
Date: 8th September 2022, 10:00 am (CEST)
Optimizing generalized Gini indices for fairness in rankings
About the speaker: Virginie Do is a PhD candidate in Computer Science at Université Paris Dauphine – PSL and Meta AI (Facebook AI Research). Her research is on fairness in machine learning and social choice theory, with a specific focus on ranking and recommender systems, and online algorithms.
Abstract: There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this work explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.
Joint work with Nicolas Usunier.