  We present evidence for the existence and effectiveness of adversarial
attacks on graph neural networks (GNNs) that aim to degrade fairness. These
attacks can disadvantage a particular subgroup of nodes in GNN-based node
classification, where nodes of the underlying network have sensitive
attributes, such as race or gender. We conduct qualitative and experimental
analyses explaining how adversarial link injection impairs the fairness of GNN
predictions. For example, an attacker can compromise the fairness of GNN-based
node classification by injecting adversarial links between nodes belonging to
opposite subgroups and opposite class labels. Our experiments on empirical
datasets demonstrate that adversarial fairness attacks can significantly
degrade the fairness of GNN predictions (attacks are effective) with a low
perturbation rate (attacks are efficient) and without a significant drop in
accuracy (attacks are deceptive). This work demonstrates the vulnerability of
GNN models to adversarial fairness attacks. We hope our findings raise
awareness about this issue in our community and lay a foundation for the future
development of GNN models that are more robust to such attacks.
