Debiaser for Multiple Variables to enhance fairness in classification tasks. Issue 2 (March 2023)

• Record Type: Journal Article
• Authors: d'Aloisio, Giordano; D'Angelo, Andrea; Di Marco, Antinisca; Stilo, Giovanni
• Is Part Of: Information processing & management. Volume 60:Issue 2(2023)
• Publication Date: 2023-03
• Subjects: Machine learning -- Bias and Fairness -- Multi-class classification -- Preprocessing algorithm -- Equality; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.103226
• Languages: English
• ISSNs: 0306-4573

Abstract

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. 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, we evaluate our method on a heterogeneous set of datasets and we show how it overcomes the established algorithms of the literature in the multi-class classification setting and in the binary classification setting when more than two sensitive variables are involved. Finally, based on the conducted experiments, we discuss strengths and weaknesses of our method and of the other baselines. Highlights: We highlight the strengths and weaknesses of established bias mitigation methods. We revise our work by generalizing the instance generation and removal strategies. We evaluate the effectiveness of different instance-generation strategies in DEMV. We evaluate DEMV by considering several datasets, methods and sensitive variables. We observe how DEMV overcomes the baselines in almost all the experiments.