Individually fair optimal decision trees

Abstract

In this paper, we tackle the problem of creating decision trees that are both optimal and individually fair. While decision trees are popular due to their interpretability, achieving optimality can be difficult. Existing approaches either lack scalability or fail to consider individual fairness. To address this, we define individual fairness as a separable optimization task by analyzing the fairness gained and lost within a sub-tree. Using the Streed framework, we implement an algorithm that constructs optimal decision trees with the lowest misclassification score and individual fairness value above a certain threshold. Our algorithm has been tested on various datasets, demonstrating its effectiveness and scalability. This research is a significant step towards creating fair decision trees that are optimal, fair, and scalable.