  Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic
decisions neither favoring nor harming certain groups defined by sensitive
attributes (e.g., race and gender), has gained considerable attention. In
particular, the objective of group fairness is to ensure that the decisions
made by GNNs are independent of the sensitive attribute. To achieve this
objective, most existing approaches involve eliminating sensitive attribute
information in node representations or algorithmic decisions. However, such
ways may also eliminate task-related information due to its inherent
correlation with the sensitive attribute, leading to a sacrifice in utility. In
this work, we focus on improving the fairness of GNNs while preserving
task-related information and propose a fair GNN framework named FairSAD.
Instead of eliminating sensitive attribute information, FairSAD enhances the
fairness of GNNs via Sensitive Attribute Disentanglement (SAD), which separates
the sensitive attribute-related information into an independent component to
mitigate its impact. Additionally, FairSAD utilizes a channel masking mechanism
to adaptively identify the sensitive attribute-related component and
subsequently decorrelates it. Overall, FairSAD minimizes the impact of the
sensitive attribute on GNN outcomes rather than eliminating sensitive
attributes, thereby preserving task-related information associated with the
sensitive attribute. Furthermore, experiments conducted on several real-world
datasets demonstrate that FairSAD outperforms other state-of-the-art methods by
a significant margin in terms of both fairness and utility performance. Our
source code is available at https://github.com/ZzoomD/FairSAD.
