Fair Adversarial Gradient Tree Boosting

TL;DR

This paper introduces a novel adversarial gradient tree boosting method that enhances fairness in decision tree models while maintaining high accuracy, addressing the gap in bias mitigation strategies for tree-based classifiers.

Contribution

The paper proposes a new adversarial gradient boosting approach that integrates fairness considerations directly into decision tree training, outperforming existing methods in accuracy and fairness.

Findings

Achieves higher accuracy than existing fair classifiers.

Maintains comparable fairness levels across multiple datasets.

Outperforms state-of-the-art algorithms in empirical evaluations.

Abstract

Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output $Y$ with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute $S$. The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on 4 popular data sets and compare against state-of-the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions.