Graph neural networks (GNNs) are being increasingly used in many high-stakes tasks, and as a result, there is growing attention on their fairness recently. GNNs have been shown to be unfair as they tend to make discriminatory decisions toward certain demographic groups, divided by sensitive attributes such as gender and race. While recent works have been devoted to improving their fairness performance, they often require accessible demographic information. This greatly limits their applicability in real-world scenarios due to legal restrictions. To address this problem, we present a demographic-agnostic method to learn fair GNNs via knowledge distillation, namely FairGKD. Our work is motivated by the empirical observation that training GNNs on partial data (i.e., only node attributes or topology data) can improve their fairness, albeit at the cost of utility. To make a balanced trade-off between fairness and utility performance, we employ a set of fairness experts (i.e., GNNs trained on different partial data) to construct the synthetic teacher, which distills fairer and informative knowledge to guide the learning of the GNN student. Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.

翻译：图神经网络（GNNs）正越来越多地应用于许多高利害任务中，因此其公平性近期受到广泛关注。研究表明GNNs可能是不公平的，因为它们倾向于基于性别和种族等敏感属性划分的人口群体做出歧视性决策。尽管近年来的工作致力于提升其公平性表现，但这些方法通常需要获取人口统计学信息，这因法律限制而严重制约了其在现实场景中的适用性。为解决这一问题，我们提出了一种无需人口统计信息的公平GNN学习方法，即通过知识蒸馏实现公平性，命名为FairGKD。本工作的动机源于实证观察：在部分数据（仅节点属性或拓扑数据）上训练GNN能够提升其公平性，但会以牺牲效用为代价。为平衡公平性与效用性能，我们采用一组公平性专家（即在不同部分数据上训练的GNN）构建合成教师模型，通过蒸馏更公平且信息量更丰富的知识来指导GNN学生模型的学习。在多个基准数据集上的实验表明，无需访问人口统计信息的FairGKD，在保持效用性的同时，能大幅提升GNN的公平性。