  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.
