FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
It addresses fairness issues in GNNs for human-centered applications, offering a novel approach beyond edge deletion, though it is incremental as it builds on existing fairness methods.
The paper tackles the problem of fairness in Graph Neural Networks (GNNs) by proposing edge addition and deletion methods, with FairEdit using gradient-based editing to improve fairness, outperforming standard training and matching state-of-the-art methods across datasets and models.
Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph. However, as GNNs are extensively used in human-centered applications, the issue of fairness has arisen. While edge deletion is a common method used to promote fairness in GNNs, it fails to consider when data is inherently missing fair connections. In this work we consider the unexplored method of edge addition, accompanied by deletion, to promote fairness. We propose two model-agnostic algorithms to perform edge editing: a brute force approach and a continuous approximation approach, FairEdit. FairEdit performs efficient edge editing by leveraging gradient information of a fairness loss to find edges that improve fairness. We find that FairEdit outperforms standard training for many data sets and GNN methods, while performing comparably to many state-of-the-art methods, demonstrating FairEdit's ability to improve fairness across many domains and models.