Towards Fair Graph Learning without Demographic Information

Abstract

Fair Graph Neural Networks (GNNs) have been extensively studied in graph-based applications. However, most approaches to fair GNNs assume the full availability of demographic information by default, which is often unrealistic due to legal restrictions or privacy concerns, leaving a noticeable gap in methods for addressing bias under such constraints. To this end, we propose a novel method for fair graph learning without demographic information. Our approach leverages a Bayesian variational autoencoder to infer missing demographic information and uses disentangled latent variables to separately capture demographics-related and label-related information, reducing interference when inferring demographic proxies. Additionally, we incorporate a fairness regularizer that enables measuring model fairness without demographics while optimizing the fairness objective. Extensive experiments on three real-world graph datasets demonstrate the proposed method's effectiveness in improving both fairness and utility.