Deceptive Fairness Attacks on Graphs via Meta Learning
Jian Kang Yinglong Xia Ross Maciejewski Jiebo Luo Hanghang Tong
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.