Fair Tree Classifier using Strong Demographic Parity

TL;DR

This paper introduces SCAFF, a novel splitting criterion for tree classifiers that optimizes for both high ROC-AUC performance and strong demographic parity fairness, accommodating multiple and intersectional sensitive attributes.

Contribution

The paper presents SCAFF, a new threshold-free criterion that enhances fair tree classification by balancing performance and fairness across multiple sensitive attributes.

Findings

SCAFF improves fairness without sacrificing accuracy.

It effectively handles multiple and intersectional sensitive attributes.

The method extends easily to bagged and boosted tree frameworks.

Abstract

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.