GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks

Abstract

Graph Neural Networks (GNNs) are playing increasingly important roles in critical decision-making scenarios due to their exceptional performance and end-to-end design. However, concerns have been raised that GNNs could make biased decisions against underprivileged groups or individuals. To remedy this issue, researchers have proposed various fairness notions including individual fairness that gives similar predictions to similar individuals. However, existing methods in individual fairness rely on Lipschitz condition: they only optimize overall individual fairness and disregard equality of individual fairness between groups. This leads to drastically different levels of individual fairness among groups. We tackle this problem by proposing a novel GNN framework GUIDE to achieve group equality informed individual fairness in GNNs. We aim to not only achieve individual fairness but also equalize the levels of individual fairness among groups. Specifically, our framework operates on the similarity matrix of individuals to learn personalized attention to achieve individual fairness without group level disparity. Comprehensive experiments on real-world datasets demonstrate that GUIDE obtains good balance of group equality informed individual fairness and model utility. The open-source implementation of GUIDE can be found here: https://github.com/mikesong724/GUIDE.