FIFA: MAKING FAIRNESS MORE GENERALIZABLE IN CLASSIFIERS TRAINED ON IMBALANCED DATA

Abstract

Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack of generalizability not only of classification, but also of fairness properties, especially in over-parameterized models. For example, fairness-aware training may ensure equalized odds (EO) on the training data, but EO is far from being satisfied on new users. In this paper, we propose a theoretically-principled, yet Flexible approach that is Imbalance-Fairness-Aware (FIFA). Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses. While our main focus is on EO, FIFA can be directly applied to achieve equalized opportunity (EqOpt); and under certain conditions, it can also be applied to other fairness notions. We demonstrate the power of FIFA by combining it with a popular fair classification algorithm, and the resulting algorithm achieves significantly better fairness generalization on several real-world datasets.

1. INTRODUCTION

Machine learning systems are becoming increasingly vital in our daily lives. The growing concern that they may inadvertently discriminate against minorities and other protected groups when identifying or allocating resources has attracted numerous attention from various communities. While significant efforts have been devoted in understanding and correcting biases in classical models such as logistic regressions and supported vector machines (SVM), see, e.g., (Agarwal et al., 2018; Hardt et al., 2016) , those derived tools are far less effective on modern over-parameterized models such as neural networks (NN). Furthermore, in large models, it is also difficult for measures of fairness (such as equalized odds to be introduced shortly) to generalize, as shown in Fig. 1 . In other



Figure 1: Each marker corresponds to a sufficiently well-trained ResNet-10 model trained on an imbalanced image classification dataset CelebA ((Liu et al., 2015)). The generalization of fairness constraints (EqualizedOdds) is substantially worse than the generalization of classification error.

