Fairness-Aware Graph Filter Design
It addresses fairness issues for under-represented groups in decision-making problems using graph data, representing an incremental improvement over existing methods.
The paper tackles bias amplification in graph-based machine learning by designing a fairness-aware graph filter, which reduces bias in node classification on real-world networks while maintaining similar utility and better stability compared to baseline algorithms.
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonstrated that ML over graphs amplifies the already existing bias towards certain under-represented groups in various decision-making problems due to the information aggregation over biased graph structures. Faced with this challenge, in this paper, we design a fair graph filter that can be employed in a versatile manner for graph-based learning tasks. The design of the proposed filter is based on a bias analysis and its optimality in mitigating bias compared to its fairness-agnostic counterpart is established. Experiments on real-world networks for node classification demonstrate the efficacy of the proposed filter design in mitigating bias, while attaining similar utility and better stability compared to baseline algorithms.