Fair-MDAV: An Algorithm for Fair Privacy by Microaggregation

Automated decision systems are being integrated to several institutions. The General Data Protection Regulation from the European Union, considers the right to explanation on such decisions, but many systems may require a group-level or community-wide analysis. However, the data on which the algorithms are trained is frequently personal data. Hence, the privacy of individuals should be protected, at the same time, ensuring the fairness of the algorithmic decisions made. In this paper we present the algorithm Fair-MDAV for privacy protection in terms of t-closeness. We show that its microaggregation procedure for privacy protection improves fairness through relabelling, while the improvement on fairness obtained equalises privacy guarantees for different groups. We perform an empirical test on Adult Dataset, carrying out the classification task of predicting whether an individual earns per year, after applying Fair-MDAV with different parameters on the training set. We observe that the accuracy of the results on the test set is well preserved, with additional guarantees of privacy and fairness.