Graph neural networks (GNNs) are increasingly used in critical human applications for predicting node labels in attributed graphs. Their ability to aggregate features from nodes' neighbors for accurate classification also has the capacity to exacerbate existing biases in data or to introduce new ones towards members from protected demographic groups. Thus, it is imperative to quantify how GNNs may be biased and to what extent their harmful effects may be mitigated. To this end, we propose two new GNN-agnostic interventions namely, (i) PFR-AX which decreases the separability between nodes in protected and non-protected groups, and (ii) PostProcess which updates model predictions based on a blackbox policy to minimize differences between error rates across demographic groups. Through a large set of experiments on four datasets, we frame the efficacies of our approaches (and three variants) in terms of their algorithmic fairness-accuracy tradeoff and benchmark our results against three strong baseline interventions on three state-of-the-art GNN models. Our results show that no single intervention offers a universally optimal tradeoff, but PFR-AX and PostProcess provide granular control and improve model confidence when correctly predicting positive outcomes for nodes in protected groups.

翻译：图神经网络（GNN）日益被用于关键的人类应用场景，以预测属性图中节点的标签。其通过聚合邻居节点特征实现精确分类的能力，也可能加剧数据中已有的偏见，或对受保护人口群体的成员引入新的偏见。因此，量化GNN可能存在的偏见及其有害效应的可缓解程度至关重要。为此，我们提出两种与GNN模型无关的新干预方法：(i) PFR-AX，该方法降低受保护与非受保护群体节点间的可分离性；(ii) PostProcess，该方法基于黑盒策略更新模型预测，以最小化不同人口群体间错误率的差异。通过在四个数据集上的大量实验，我们从算法公平性-精度权衡的角度评估了所提方法（及三种变体）的有效性，并将结果与三种强基线干预方法在三个最先进的GNN模型上进行了对比。结果表明，没有任何单一干预方法能提供全局最优的权衡，但PFR-AX与PostProcess能提供细粒度控制，并在正确预测受保护群体节点正向结果时提升模型置信度。