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.
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EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed networks in various web-based applications such as social recommendation and web search. Nevertheless, in high-stake decision-making scenarios such as online fraud detection, there is an increasing societal concern that GNNs could make discriminatory decisions towards certain demographic groups. Despite recent explorations on fair GNNs, these works are tailored for a specific GNN model. However, myriads of GNN variants have been proposed for different applications, and it is costly to fine-tune existing debiasing algorithms for each specific GNN architecture. Different from existing works that debias GNN models, we aim to debias the input attributed network to achieve fairer GNNs through feeding GNNs with less biased data. Specifically, we propose novel definitions and metrics to measure the bias in an attributed network, which leads to the optimization objective to mitigate bias. We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks. EDITS works in a model-agnostic manner, i.e., it is independent of any specific GNN. Experiments demonstrate the validity of the proposed bias metrics and the superiority of EDITS on both bias mitigation and utility maintenance. Open-source implementation: https://github.com/yushundong/EDITS.
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- Award ID(s):
- 2006844
- NSF-PAR ID:
- 10357534
- Date Published:
- Journal Name:
- Proceedings of the ACM Web Conference 2022
- Page Range / eLocation ID:
- 1259 to 1269
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3485447.3512173
