Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Although some work has developed fair GNNs, most of them directly borrow fair representation learning techniques from non-graph domains without considering the potential problem of sensitive attribute leakage caused by feature propagation in GNNs. However, we empirically observe that feature propagation could vary the correlation of previously innocuous non-sensitive features to the sensitive ones. This can be viewed as a leakage of sensitive information which could further exacerbate discrimination in predictions. Thus, we design two feature masking strategies according to feature correlations to highlight the importance of considering feature propagation and correlation variation in alleviating discrimination. Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation. Given the learned fair views, we adaptively clamp weights of the encoder to avoid using sensitive-related features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a better trade-off between model utility and fairness. Our code is publicly available at \href{https://github.com/YuWVandy/FairVGNN}{\textcolor{blue}{https://github.com/YuWVandy/FairVGNN}}.
翻译:神经网络(GNNs)在图表中学习节点表示显示出巨大的力量。然而,它们可能继承培训数据的历史偏见,导致预测中的歧视性偏见。虽然有些工作已经开发出公平的GNS, 其中大部分直接从非绘图领域借用了公平的代表性学习技术,而没有考虑到GNNs地貌传播可能造成的敏感属性渗漏的潜在问题。然而,我们从经验中观察到,地貌传播可能会改变先前不显眼的非敏感特性与敏感特性的相互关系。这可被视为敏感信息的渗漏,可能进一步加剧预测中的歧视。因此,我们根据地物相关关系设计了两种特性掩蔽战略,以突出考虑特征传播和相关性变化在减少歧视方面的重要性。我们的分析激励了公平观点,我们提议通过自动识别和遮掩与敏感相关特性有关的特性,从而考虑到地貌传播之后的关联性变化。鉴于所了解的公平观点,我们适应性地夹紧了编码,从而避免使用与敏感有关的特性。我们在现实-世界数据传播和公平性 G.FairVru_Fral_Bral_V_BAR_BAR_G在公共数据库中显示我们的公平性。