Deceptive Fairness Attacks on Graphs via Meta Learning
This work addresses adversarial robustness in fair graph learning, which is an incremental contribution to security and fairness in machine learning.
The paper tackles the problem of deceptive fairness attacks on graph learning models, proposing a meta learning-based framework called FATE that amplifies bias in graph neural networks while maintaining utility, with experimental results showing effectiveness on real-world datasets.
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.