  We address the growing apprehension that GNNs, in the absence of fairness
constraints, might produce biased decisions that disproportionately affect
underprivileged groups or individuals. Departing from previous work, we
introduce for the first time a method for incorporating the Gini coefficient as
a measure of fairness to be used within the GNN framework. Our proposal,
GRAPHGINI, works with the two different goals of individual and group fairness
in a single system, while maintaining high prediction accuracy. GRAPHGINI
enforces individual fairness through learnable attention scores that help in
aggregating more information through similar nodes. A heuristic-based maximum
Nash social welfare constraint ensures the maximum possible group fairness.
Both the individual fairness constraint and the group fairness constraint are
stated in terms of a differentiable approximation of the Gini coefficient. This
approximation is a contribution that is likely to be of interest even beyond
the scope of the problem studied in this paper. Unlike other state-of-the-art,
GRAPHGINI automatically balances all three optimization objectives (utility,
individual, and group fairness) of the GNN and is free from any manual tuning
of weight parameters. Extensive experimentation on real-world datasets
showcases the efficacy of GRAPHGINI in making significant improvements in
individual fairness compared to all currently available state-of-the-art
methods while maintaining utility and group equality.
