  Graph Neural Networks (GNNs) have shown great power in learning node
representations on graphs. However, they may inherit historical prejudices from
training data, leading to discriminatory bias in predictions. Although some
work has developed fair GNNs, most of them directly borrow fair representation
learning techniques from non-graph domains without considering the potential
problem of sensitive attribute leakage caused by feature propagation in GNNs.
However, we empirically observe that feature propagation could vary the
correlation of previously innocuous non-sensitive features to the sensitive
ones. This can be viewed as a leakage of sensitive information which could
further exacerbate discrimination in predictions. Thus, we design two feature
masking strategies according to feature correlations to highlight the
importance of considering feature propagation and correlation variation in
alleviating discrimination. Motivated by our analysis, we propose Fair View
Graph Neural Network (FairVGNN) to generate fair views of features by
automatically identifying and masking sensitive-correlated features considering
correlation variation after feature propagation. Given the learned fair views,
we adaptively clamp weights of the encoder to avoid using sensitive-related
features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a
better trade-off between model utility and fairness. Our code is publicly
available at https://github.com/YuWVandy/FairVGNN.
