Fairness-aware feature selection: A causal path approach

The real-world datasets used for training machine learning models may contain sensitive features such as gender, race, and age, raising concerns about unfair treatment of specific groups or individuals. These concerns can frequently be alleviated by feature selection techniques. However, most existing research primarily focuses on removing statistically correlated features with sensitive attributes, which frequently fails to address the underlying causal relationships that contribute to unfair treatment. In this paper, we propose a novel fairness-aware causal feature selection method (FCFS). By constructing a causal diagram and leveraging Markov blanket theory, we identify the discrimination-prone feature set that undermine the fairness of model decisions, then propose direct and indirect causal discriminatory effect measures for each feature within the set. Features with high discrimination effect are iteratively assigned priority for removal. Experimental results on public datasets, including Adult, Boston, and German, demonstrate that the proposed method, FCFS, achieves an ideal tradeoff between accuracy and fairness (measured by EOs, DP, EO, and BAD) compared to existing fairness-aware feature selection methods.