  As machine learning becomes more widely adopted across domains, it is
critical that researchers and ML engineers think about the inherent biases in
the data that may be perpetuated by the model. Recently, many studies have
shown that such biases are also imbibed in Graph Neural Network (GNN) models if
the input graph is biased, potentially to the disadvantage of underserved and
underrepresented communities. In this work, we aim to mitigate the bias learned
by GNNs by jointly optimizing two different loss functions: one for the task of
link prediction and one for the task of demographic parity. We further
implement three different techniques inspired by graph modification approaches:
the Global Fairness Optimization (GFO), Constrained Fairness Optimization
(CFO), and Fair Edge Weighting (FEW) models. These techniques mimic the effects
of changing underlying graph structures within the GNN and offer a greater
degree of interpretability over more integrated neural network methods. Our
proposed models emulate microscopic or macroscopic edits to the input graph
while training GNNs and learn node embeddings that are both accurate and fair
under the context of link recommendations. We demonstrate the effectiveness of
our approach on four real world datasets and show that we can improve the
recommendation fairness by several factors at negligible cost to link
prediction accuracy.
