MAPPING: debiasing graph neural networks for fair node classification with limited sensitive information leakage

Despite remarkable success in diverse web-based applications, Graph Neural Networks (GNNs) inherit and further exacerbate historical discrimination and social stereotypes, which critically hinder their deployments in high-stake domains such as online clinical diagnosis, financial crediting, etc. However, existing research in fair graph learning typically favors pairwise constraints to achieve fairness but fails to cast off dimensional limitations and generalize them into multiple sensitive attributes. Besides, most studies focus on in-processing techniques to enforce and calibrate fairness, constructing a model-agnostic debiasing GNN framework at the pre-processing stage to prevent downstream misuses and improve training reliability is still largely under-explored. Furthermore, previous work tends to enhance either fairness or privacy individually but few probes into how fairness issues trigger privacy concerns and whether such concerns can be alleviated with fairness intervention. In this paper, we propose a novel model-agnostic debiasing framework named MAPPING (Masking And Pruning and Message-Passing trainING) for fair node classification, in which we adopt the distance covariance (dCov)-based fairness constraints to simultaneously reduce feature and topology biases under multiple sensitive memberships, and combine them with adversarial debiasing to confine the risks of sensitive attribute inference. Experiments on real-world datasets with different GNN variants demonstrate the effectiveness and flexibility of MAPPING. Our results show that MAPPING can achieve better trade-offs between utility and fairness, and mitigate privacy risks of sensitive information leakage. This work paves the way for a new direction in trustworthy GNNs by addressing fairness and privacy concerns simultaneously, rather than achieving fairness at the expense of privacy.