Fair Adversarial Gradient Tree Boosting
This addresses fairness in classification for tabular data, where tree-based methods are efficient but understudied, offering an incremental improvement over existing techniques.
The authors tackled the problem of fair classification in tabular data by developing adversarial gradient tree boosting, which integrates an adversarial neural network to minimize prediction of sensitive attributes while maintaining accuracy. Results on four datasets show the algorithm achieves higher accuracy with comparable fairness to state-of-the-art methods.
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.