We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? We answer this question via a bi-level optimization problem and propose a meta learning-based framework named FATE. FATE is broadly applicable with respect to various fairness definitions and graph learning models, as well as arbitrary choices of manipulation operations. We further instantiate FATE to attack statistical parity and individual fairness on graph neural networks. We conduct extensive experimental evaluations on real-world datasets in the task of semi-supervised node classification. The experimental results demonstrate that FATE could amplify the bias of graph neural networks with or without fairness consideration while maintaining the utility on the downstream task. We hope this paper provides insights into the adversarial robustness of fair graph learning and can shed light on designing robust and fair graph learning in future studies.

翻译：我们研究图上的欺骗性公平攻击，旨在回答以下问题：如何对图学习模型进行投毒攻击，以欺骗性地加剧偏差？我们通过一个双层优化问题来回答这个问题，并提出了一个名为FATE的基于元学习的框架。FATE广泛适用于各种公平性定义和图学习模型，以及任意选择的操纵操作。我们进一步将FATE实例化，用于攻击图神经网络的统计平等性和个体公平性。在半监督节点分类任务中，我们在真实数据集上进行了广泛的实验评估。实验结果表明，FATE可以在保持下游任务效用的同时，放大图神经网络的偏差，无论是否考虑了公平性。我们希望本文能为公平图学习的对抗鲁棒性提供见解，并有助于未来研究中设计鲁棒且公平的图学习。