EQUAL IMPROVABILITY: A NEW FAIRNESS NOTION CONSIDERING THE LONG-TERM IMPACT

Abstract

Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Recently, effort-based fairness notions are getting attention, which considers the scenarios of each individual making effort to improve its feature over time. Such scenarios happen in the real world, e.g., college admission and credit loaning, where each rejected sample makes effort to change its features to get accepted afterward. In this paper, we propose a new effortbased fairness notion called Equal Improvability (EI), which equalizes the potential acceptance rate of the rejected samples across different groups assuming a bounded level of effort will be spent by each rejected sample. We also propose and study three different approaches for finding a classifier that satisfies the EI requirement. Through experiments on both synthetic and real datasets, we demonstrate that the proposed EI-regularized algorithms encourage us to find a fair classifier in terms of EI. Additionally, we ran experiments on dynamic scenarios which highlight the advantages of our EI metric in equalizing the distribution of features across different groups, after the rejected samples make some effort to improve. Finally, we provide mathematical analyses of several aspects of EI: the relationship between EI and existing fairness notions, and the effect of EI in dynamic scenarios. Codes are available in a GitHub repository 1 .

1. INTRODUCTION

Over the past decade, machine learning has been used in a wide variety of applications. However, these machine learning approaches are observed to be unfair to individuals having different ethnicity, race, and gender. As the implicit bias in artificial intelligence tools raised concerns over potential discrimination and equity issues, various researchers suggested defining fairness notions and developing classifiers that achieve fairness. One popular fairness notion is demographic parity (DP), which requires the decision-making system to provide output such that the groups are equally likely to be assigned to the desired prediction classes, e.g., acceptance in the admission procedure. DP and related fairness notions are largely employed to mitigate the bias in many realistic problems such as recruitment, credit lending, and university admissions (Zafar et al., 2017b; Hardt et al., 2016; Dwork et al., 2012; Zafar et al., 2017a) . However, most of the existing fairness notions only focus on immediate fairness, without taking potential follow-up inequity risk into consideration. In Fig. 1 , we provide an example scenario when using DP fairness has a long-term fairness issue, in a simple loan approval problem setting. Consider two groups (group 0 and group 1) with different distributions, where each individual has one label (approve the loan or not) and two features (credit score, income) that can be improved over time. Suppose each group consists of two clusters (with three samples each), and the distance between the clusters is different for two groups. Fig. 1 visualizes the distributions of two groups and the decision boundary of a classifier f which achieves DP among the groups. We observe that the rejected samples (left-hand-side of the decision boundary) in group 1 are located further away from the decision boundary than the rejected samples in group 0. As a result, the rejected applicants in group 1 need to make more effort to cross the decision boundary and get approval. This improvability gap between the two groups can make the rejected applicants in group 1 less motivated to improve their features, which may increase the gap between different groups in the future.

