  When dealing with sensitive data in automated data-driven decision-making, an
important concern is to learn predictors with high performance towards a class
label, whilst minimising for the discrimination towards any sensitive
attribute, like gender or race, induced from biased data. A few hybrid tree
optimisation criteria exist that combine classification performance and
fairness. Although the threshold-free ROC-AUC is the standard for measuring
traditional classification model performance, current fair tree classification
methods mainly optimise for a fixed threshold on both the classification task
as well as the fairness metric. In this paper, we propose a compound splitting
criterion which combines threshold-free (i.e., strong) demographic parity with
ROC-AUC termed SCAFF -- Splitting Criterion AUC for Fairness -- and easily
extends to bagged and boosted tree frameworks. Our method simultaneously
leverages multiple sensitive attributes of which the values may be
multicategorical or intersectional, and is tunable with respect to the
unavoidable performance-fairness trade-off. In our experiments, we demonstrate
how SCAFF generates models with performance and fairness with respect to
binary, multicategorical, and multiple sensitive attributes.
