Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification

TL;DR

This paper investigates biases in graph neural networks for node classification, proposing two interventions to mitigate disparities while balancing accuracy, and benchmarks their effectiveness across multiple datasets and models.

Contribution

Introduces two GNN-agnostic interventions, PFR-AX and PostProcess, to reduce bias and improve fairness in node classification tasks.

Findings

PFR-AX decreases bias but may affect accuracy.

PostProcess reduces error rate disparities.

No single method is universally optimal for fairness-accuracy tradeoff.

Abstract

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.