Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage. (arXiv:2206.03426v2 [cs.LG] UPDATED)

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 …

fairness lg networks