FairXGBoost: Fairness-aware Classification in XGBoost
  Highly regulated domains such as finance have long favoured the use of
machine learning algorithms that are scalable, transparent, robust and yield
better performance. One of the most prominent examples of such an algorithm is
XGBoost. Meanwhile, there is also a growing interest in building fair and
unbiased models in these regulated domains and numerous bias-mitigation
algorithms have been proposed to this end. However, most of these
bias-mitigation methods are restricted to specific model families such as
logistic regression or support vector machine models, thus leaving modelers
with a difficult decision of choosing between fairness from the bias-mitigation
algorithms and scalability, transparency, performance from algorithms such as
XGBoost. We aim to leverage the best of both worlds by proposing a fair variant
of XGBoost that enjoys all the advantages of XGBoost, while also matching the
levels of fairness from the state-of-the-art bias-mitigation algorithms.
Furthermore, the proposed solution requires very little in terms of changes to
the original XGBoost library, thus making it easy for adoption. We provide an
empirical analysis of our proposed method on standard benchmark datasets used
in the fairness community.
