  Graph neural networks (GNNs) have shown great power in modeling graph
structured data. However, similar to other machine learning models, GNNs may
make predictions biased on protected sensitive attributes, e.g., skin color and
gender. Because machine learning algorithms including GNNs are trained to
reflect the distribution of the training data which often contains historical
bias towards sensitive attributes. In addition, the discrimination in GNNs can
be magnified by graph structures and the message-passing mechanism. As a
result, the applications of GNNs in sensitive domains such as crime rate
prediction would be largely limited. Though extensive studies of fair
classification have been conducted on i.i.d data, methods to address the
problem of discrimination on non-i.i.d data are rather limited. Furthermore,
the practical scenario of sparse annotations in sensitive attributes is rarely
considered in existing works. Therefore, we study the novel and important
problem of learning fair GNNs with limited sensitive attribute information.
FairGNN is proposed to eliminate the bias of GNNs whilst maintaining high node
classification accuracy by leveraging graph structures and limited sensitive
information. Our theoretical analysis shows that FairGNN can ensure the
fairness of GNNs under mild conditions given limited nodes with known sensitive
attributes. Extensive experiments on real-world datasets also demonstrate the
effectiveness of FairGNN in debiasing and keeping high accuracy.
