Empirical Evaluation of Counterfactual Fairness in the Context of Statistical Fairness
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2019-06-01
Language
en
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Abstract
Machine learning is on the rise and has been in the past decade. Only in the
last few years, a distinct focus on fairness in machine learning has surfaced.
As machine learning systems become more in
uential and widespread, the
need arises to ensure that decisions that follow from these systems are fair.
A variety of fairness de nitions have already been proposed to serve that
purpose. Counterfactual fairness is one such de nition and is the focal point
of this study. The abundant fairness de nitions that sometimes clash can
complicate the realization of fairness. In this study, counterfactual fairness
is examined in conjunction with statistical fairness to investigate whether
counterfactual fairness also allows for statistical fairness. A counterfactually
fair predictor is constructed on a real-world data set about loan applicants,
and this predictor is empirically evaluated on a set of statistical fairness
de nitions. The results suggest that counterfactual fairness can promote
statistical fairness depending on which de nitions are considered.
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Faculteit der Sociale Wetenschappen