Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. 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, a novel approach able to enhance fairness in both binary and multi-class classification problems. The proposed method is compared, under several conditions, with the well-established baseline. We evaluate our method on a heterogeneous data set and prove how it overcomes the established algorithms in the multi-classification setting, while maintaining good performances in binary classification. Finally, we present some limitations and future improvements.

Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach

d'Aloisio G.
;
Stilo G.;Di Marco A.;D'Angelo A.
2022-01-01

Abstract

Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. 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, a novel approach able to enhance fairness in both binary and multi-class classification problems. The proposed method is compared, under several conditions, with the well-established baseline. We evaluate our method on a heterogeneous data set and prove how it overcomes the established algorithms in the multi-classification setting, while maintaining good performances in binary classification. Finally, we present some limitations and future improvements.
978-3-031-09315-9
978-3-031-09316-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/195039
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