Fairness-Aware Decision Tree Editing Based on Mixed- Integer Linear Optimization

Abstract

In the application of machine learning models to decision-making tasks (e.g., loan approval), fairness of their predictions has emerged as an important topic in recent years. If decision-makers detect unfairness in their models during deployment, they must modify the models to satisfy constraints on a specific discrimination criterion. However, simply retraining a model from scratch under fairness constraints may raise serious reliability issues caused by differences in prediction and interpretation between the initial model and retrained model. In this paper, we propose a post-processing framework, named Fairness-Aware Decision tree Editing (FADE), that converts a given biased decision tree into a fair decision tree without significantly changing it in terms of its prediction and interpretation. For this purpose, we introduce two dissimilarity measures between decision trees based on the prediction discrepancy and edit distance. We propose a mixed-integer linear optimization formulation for minimizing the dissimilarity measures under fairness constraints. Numerical experiments on real datasets demonstrate the effectiveness of our method in comparison with existing methods.