The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer
This addresses fairness in graph modeling for applications involving human relations, offering a more flexible and generalizable approach compared to prior incremental work.
The authors tackled the problem of ensuring fairness in probabilistic graph models by proposing a generic fairness regularizer based on the KL-divergence between a model and its fair I-projection, which efficiently trades off fairness with accuracy across various fairness criteria, unlike existing methods limited to specific criteria.
Learning and reasoning over graphs is increasingly done by means of probabilistic models, e.g. exponential random graph models, graph embedding models, and graph neural networks. When graphs are modeling relations between people, however, they will inevitably reflect biases, prejudices, and other forms of inequity and inequality. An important challenge is thus to design accurate graph modeling approaches while guaranteeing fairness according to the specific notion of fairness that the problem requires. Yet, past work on the topic remains scarce, is limited to debiasing specific graph modeling methods, and often aims to ensure fairness in an indirect manner. We propose a generic approach applicable to most probabilistic graph modeling approaches. Specifically, we first define the class of fair graph models corresponding to a chosen set of fairness criteria. Given this, we propose a fairness regularizer defined as the KL-divergence between the graph model and its I-projection onto the set of fair models. We demonstrate that using this fairness regularizer in combination with existing graph modeling approaches efficiently trades-off fairness with accuracy, whereas the state-of-the-art models can only make this trade-off for the fairness criterion that they were specifically designed for.