Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the de facto solution in a variety of decision-making scenarios. However, GNNs could yield biased results against certain demographic subgroups. Some recent works have empirically shown that the biased structure of the input network is a significant source of bias for GNNs. Nevertheless, no studies have systematically scrutinized which part of the input network structure leads to biased predictions for any given node. The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios. In this paper, we study a novel research problem of structural explanation of bias in GNNs. Specifically, we propose a novel post-hoc explanation framework to identify two edge sets that can maximally account for the exhibited bias and maximally contribute to the fairness level of the GNN prediction for any given node, respectively. Such explanations not only provide a comprehensive understanding of bias/fairness of GNN predictions but also have practical significance in building an effective yet fair GNN model. Extensive experiments on real-world datasets validate the effectiveness of the proposed framework towardsmore »
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
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 more »
- Publication Date:
- NSF-PAR ID:
- 10355932
- Journal Name:
- ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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