  Graph neural networks (GNNs) are increasingly used in critical human
applications for predicting node labels in attributed graphs. Their ability to
aggregate features from nodes' neighbors for accurate classification also has
the capacity to exacerbate existing biases in data or to introduce new ones
towards members from protected demographic groups. Thus, it is imperative to
quantify how GNNs may be biased and to what extent their harmful effects may be
mitigated. To this end, we propose two new GNN-agnostic interventions namely,
(i) PFR-AX which decreases the separability between nodes in protected and
non-protected groups, and (ii) PostProcess which updates model predictions
based on a blackbox policy to minimize differences between error rates across
demographic groups. Through a large set of experiments on four datasets, we
frame the efficacies of our approaches (and three variants) in terms of their
algorithmic fairness-accuracy tradeoff and benchmark our results against three
strong baseline interventions on three state-of-the-art GNN models. Our results
show that no single intervention offers a universally optimal tradeoff, but
PFR-AX and PostProcess provide granular control and improve model confidence
when correctly predicting positive outcomes for nodes in protected groups.
