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Debiaser for Multiple Variables to enhance fairness in classification tasks by Giordano d’Aloisio; Andrea D’Angelo; Antinisca Di Marco; Giovanni Stilo is a Computer Science article available to read on EtoBox.

What is Debiaser for Multiple Variables to enhance fairness in classification tasks about?

Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. This is also highlighted by some of the sustainable development goals proposed by the United Nations. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables (DEMV), an approach able to mitigate unbalanced groups bias (i.e., bias caused by an unequal distribution of instances in the population) in both binary and multi-class classification problems with multiple sensitive variables. The proposed method is compared, under several conditions, with a set of well-established baselines using different categories of classifiers. At first we conduct a specific study to understand which is the best generation strategies and their impact on DEMV’s ability to improve fairness. Then, w

Who reads Debiaser for Multiple Variables to enhance fairness in classification tasks?

Author

Giordano d’Aloisio; Andrea D’Angelo; Antinisca Di Marco; Giovanni Stilo

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)