FAIRGRAD: FAIRNESS AWARE GRADIENT DESCENT

Abstract

We address the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population. Most existing approaches are limited to simple binary tasks or involve difficult to implement training mechanisms. This reduces their practical applicability. In this paper, we propose FairGrad, a method to enforce fairness based on a reweighting scheme that iteratively learns group specific weights based on whether they are advantaged or not. FairGrad is easy to implement and can accommodate various standard fairness definitions. Furthermore, we show that it is competitive with standard baselines over various datasets including ones used in natural language processing and computer vision.

1. INTRODUCTION

Fair Machine Learning addresses the problem of learning models that are free of any discriminatory behavior against a subset of the population. For instance, consider a company that develops a model to predict whether a person would be a suitable hire based on their biography. A possible source of discrimination here can be if, in the data available to the company, individuals that are part of a subgroup formed based on their gender, ethnicity, or other sensitive attributes, are consistently labelled as unsuitable hires regardless of their true competency due to historical bias. This kind of discrimination can be measured by a fairness notion called Demographic Parity (Calders et al., 2009) . If the data is unbiased, another source of discriminate may stem from the model itself that consistently mislabel the competent individuals of a subgroup as unsuitable hires. This can be measured by a fairness notion called Equality of Opportunity (Hardt et al., 2016) . Several such fairness notions have been proposed in the literature as different problems call for different measures. These notions can be divided into two major paradigms, namely (i) Individual Fairness (Dwork et al., 2012; Kusner et al., 2017) where the idea is to treat similar individuals similarly regardless of the sensitive group they belong to, and (ii) Group Fairness (Calders et al., 2009; Hardt et al., 2016; Zafar et al., 2017a; Denis et al., 2021) where the underlying idea is that different sensitive groups should not be disadvantaged compared to an overall reference population. In this paper, we focus on group fairness in the context of classification. The existing approaches for group fairness in Machine Learning may be divided into three main paradigms. First, pre-processing methods aim at modifying a dataset to remove any intrinsic unfairness that may exist in the examples. The underlying idea is that a model learned on this modified data is more likely to be fair (Dwork et al., 2012; Kamiran & Calders, 2012; Zemel et al., 2013; Feldman et al., 2015; Calmon et al., 2017) . Then, post-processing approaches modify the predictions of an accurate but unfair model so that it becomes fair (Kamiran et al., 2010; Hardt et al., 2016; Woodworth et al., 2017; Iosifidis et al., 2019; Chzhen et al., 2019) . Finally, in-processing methods aim at learning a model that is fair and accurate in a single step (Calders & Verwer, 2010; Kamishima et al., 2012; Goh et al., 2016; Zafar et al., 2017a; b; Donini et al., 2018; Krasanakis et al., 2018; Agarwal et al., 2018; Wu et al., 2019; Cotter et al., 2019; Iosifidis & Ntoutsi, 2019; Jiang & Nachum, 2020; Lohaus et al., 2020; Roh et al., 2020; Ozdayi et al., 2021) . In this paper, we propose a new in-processing approach based on a reweighting scheme that may also be used as a kind of post-processing approach by fine-tuning existing classifiers. Motivation. In-processing approaches can be further divided into several sub-categories (Caton & Haas, 2020) . Common amongst them are methods that relax the fairness constraints under consideration to simplify the learning process (Zafar et al., 2017a; Donini et al., 2018; Wu et al., 2019) .

