  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.
