The Devil is in the Data: Learning Fair Graph Neural Networks via
  Partial Knowledge Distillation
Yuchang Zhu Jintang Li Liang Chen Zibin Zheng
Graph neural networks (GNNs) are being increasingly used in many high-stakes
tasks, and as a result, there is growing attention on their fairness recently.
GNNs have been shown to be unfair as they tend to make discriminatory decisions
toward certain demographic groups, divided by sensitive attributes such as
gender and race. While recent works have been devoted to improving their
fairness performance, they often require accessible demographic information.
This greatly limits their applicability in real-world scenarios due to legal
restrictions. To address this problem, we present a demographic-agnostic method
to learn fair GNNs via knowledge distillation, namely FairGKD. Our work is
motivated by the empirical observation that training GNNs on partial data
(i.e., only node attributes or topology data) can improve their fairness,
albeit at the cost of utility. To make a balanced trade-off between fairness
and utility performance, we employ a set of fairness experts (i.e., GNNs
trained on different partial data) to construct the synthetic teacher, which
distills fairer and informative knowledge to guide the learning of the GNN
student. Experiments on several benchmark datasets demonstrate that FairGKD,
which does not require access to demographic information, significantly
improves the fairness of GNNs by a large margin while maintaining their
utility.