Evolutionary Algorithms for Fair Machine Learning

At present, supervised machine learning algorithms are ubiquitously used to learn predictive models that have a major impact on people’s lives. However, the vast majority of such algorithms were developed to optimise predictive accuracyPredictive accuracy only, ignoring the issue of fairnessFairness in the predictions of the learned models. This often leads to unfair predictive models, since real-world data usually contains biasBias or prejudices against certain groups of individuals (e.g. some gender or race). Hence, an increasingly important research area involves fairness-aware machine learning algorithms, i.e. algorithms that optimise both the predictive accuracyPredictive accuracy and the fairnessFairness of their learned predictive models, from a multi-objective optimisation perspective. In this chapter, we review fairness-aware Evolutionary Algorithms (EAs) for supervised machine learning. We first briefly provide some background concepts on fairness measuresFairness measures and multi-objective optimisation approaches. Then, we review six EAs for fairness-aware machine learning, which are in general based on multi-objective optimisation principles. The reviewed EAs address a variety of supervised machine learning tasks, namely: three EAs address a data pre-processing task for classification (one addressing feature constructionFeature construction and two addressing feature selectionFeature selection); one EA optimises the hyper-parameters of a base classification algorithm; one EA evolves an ensemble of artificial neural network models; and one EA finds fair counterfactualsCounterfactuals. We conclude with a summary of the main findings of this review and some suggested future research directions.