Fairness in Graph Convolutional Neural Networks (GCNs) becomes a more and more important concern as GCNs are adopted in many crucial applications. Societal biases against sensitive groups may exist in many real world graphs. GCNs trained on those graphs may be vulnerable to being affected by such biases. In this paper, we adopt the well-known fairness notion of demographic parity and tackle the challenge of training fair and accurate GCNs efficiently. We present an in-depth analysis on how graph structure bias, node attribute bias, and model parameters may affect the demographic parity of GCNs. Our insights lead to FairSample, a framework that jointly mitigates the three types of biases. We employ two intuitive strategies to rectify graph structures. First, we inject edges across nodes that are in different sensitive groups but similar in node features. Second, to enhance model fairness and retain model quality, we develop a learnable neighbor sampling policy using reinforcement learning. To address the bias in node features and model parameters, FairSample is complemented by a regularization objective to optimize fairness.

翻译：图卷积神经网络（GCNs）在众多关键应用中的广泛采用，使其公平性成为日益重要的关注点。现实世界的许多图数据可能隐含着针对敏感群体的社会偏见，基于这些图训练的GCNs易受此类偏见影响。本文采用广为认可的群体公平性概念——人口统计均等，致力于高效训练兼具公平性与准确性的GCNs。我们深入分析了图结构偏差、节点属性偏差以及模型参数如何影响GCNs的人口统计均等性。基于这些洞见，我们提出了FairSample框架，该框架能够协同缓解三类偏差。我们采用两种直观策略来修正图结构：首先，在处于不同敏感群体但节点特征相似的节点之间注入边；其次，为增强模型公平性并保持模型质量，我们利用强化学习开发了一种可学习的邻居采样策略。针对节点特征和模型参数中的偏差，FairSample通过正则化目标函数进一步优化公平性。