Less Is More: Feature Engineering for Fairness and Performance of Machine Learning Software

Machine Learning (ML) software employs statistical algorithms to perform high-stake tasks in our daily lives, whose results are usually discriminatory due to protected features (e.g., gender), i.e., one part (called privileged, e.g., male) may be more likely to obtain beneficial decisions than the other part (called unprivileged, e.g., female). In alleviating the unfairness, developers have obtained widely held beliefs about the tradeoff between performance and fairness for ML software. Surprisingly, recent research on feature engineering suggests that enlarging the feature set is the perfect way to kill two birds with one stone, i.e., achieving both higher performance and fairness. However, the experiments used in the prior study did not remove the effect of protected features, which have been suggested to be excluded in both industrial applications and academic studies. As a result, the study did not fully explore the tradeoff between performance and fairness. In this article, we first conduct an empirical study to replicate this prior study after excluding the protected features and observe that there is still a tradeoff between performance and fairness with enlarging the features, i.e., more features are not perfect, which would lead to higher performance and lower fairness. Due to more features causing more collection and pre-processing budgets, we aim to search for an effective alternative. Inspired by the “less is more” principle, we propose a novel feature ranking method, Hybrid-importance and Early-validation based Feature Ranking (HEFR) , to find an efficient subset to replace the full feature set with comparable performance and fairness. Our method, HEFR, employs hybrid feature importances to combine performance and fairness and conducts early validation to check the effectiveness of hybrid importances. We conduct experiments on seven datasets and three classifiers to evaluate our method with five baselines. The results have shown that (a) HEFR is efficient for ML software feature engineering: applying HEFR to choose about 10% of features would construct ML software with better or comparable performance and fairness, and (b) HEFR is actionable with small dataset sizes: applying HEFR with only 10% data size would still help choose the proper feature subset.