  Graph neural networks (GNNs), has been widely used for supervised learning
tasks in graphs reaching state-of-the-art results. However, little work was
dedicated to creating unbiased GNNs, i.e., where the classification is
uncorrelated with sensitive attributes, such as race or gender. Some ignore the
sensitive attributes or optimize for the criteria of statistical parity for
fairness. However, it has been shown that neither approaches ensure fairness,
but rather cripple the utility of the prediction task. In this work, we present
a GNN framework that allows optimizing representations for the notion of
Equalized Odds fairness criteria. The architecture is composed of three
components: (1) a GNN classifier predicting the utility class, (2) a sampler
learning the distribution of the sensitive attributes of the nodes given their
labels. It generates samples fed into a (3) discriminator that discriminates
between true and sampled sensitive attributes using a novel "permutation loss"
function. Using these components, we train a model to neglect information
regarding the sensitive attribute only with respect to its label. To the best
of our knowledge, we are the first to optimize GNNs for the equalized odds
criteria. We evaluate our classifier over several graph datasets and sensitive
attributes and show our algorithm reaches state-of-the-art results.
