Making ML models fairer through explanations: the case of LimeOut

TL;DR

This paper proposes a simple ensemble method called LimeOut that uses feature dropout and explanations to improve fairness of ML models without sacrificing accuracy, addressing biases related to sensitive features.

Contribution

It introduces LimeOut, a novel approach combining feature dropout and ensemble techniques to enhance fairness in ML models based on explanation-driven assessments.

Findings

LimeOut reduces model reliance on sensitive features.

LimeOut maintains or improves fairness metrics.

LimeOut preserves classifier accuracy.

Abstract

Algorithmic decisions are now being used on a daily basis, and based on Machine Learning (ML) processes that may be complex and biased. This raises several concerns given the critical impact that biased decisions may have on individuals or on society as a whole. Not only unfair outcomes affect human rights, they also undermine public trust in ML and AI. In this paper we address fairness issues of ML models based on decision outcomes, and we show how the simple idea of "feature dropout" followed by an "ensemble approach" can improve model fairness. To illustrate, we will revisit the case of "LimeOut" that was proposed to tackle "process fairness", which measures a model's reliance on sensitive or discriminatory features. Given a classifier, a dataset and a set of sensitive features, LimeOut first assesses whether the classifier is fair by checking its reliance on sensitive features using "Lime explanations". If deemed unfair, LimeOut then applies feature dropout to obtain a pool of classifiers. These are then combined into an ensemble classifier that was empirically shown to be less dependent on sensitive features without compromising the classifier's accuracy. We present different experiments on multiple datasets and several state of the art classifiers, which show that LimeOut's classifiers improve (or at least maintain) not only process fairness but also other fairness metrics such as individual and group fairness, equal opportunity, and demographic parity.