EXCHANGING LESSONS BETWEEN ALGORITHMIC FAIRNESS AND DOMAIN GENERALIZATION Anonymous

Abstract

Standard learning approaches are designed to perform well on average for the data distribution available at training time. Developing learning approaches that are not overly sensitive to the training distribution is central to research on domainor out-of-distribution generalization, robust optimization and fairness. In this work we focus on links between research on domain generalization and algorithmic fairness-where performance under a distinct but related test distributions is studied-and show how the two fields can be mutually beneficial. While domain generalization methods typically rely on knowledge of disjoint "domains" or "environments", "sensitive" label information indicating which demographic groups are at risk of discrimination is often used in the fairness literature. Drawing inspiration from recent fairness approaches that improve worst-case performance without knowledge of sensitive groups, we propose a novel domain generalization method that handles the more realistic scenario where environment partitions are not provided. We then show theoretically and empirically how different partitioning schemes can lead to increased or decreased generalization performance, enabling us to outperform Invariant Risk Minimization with handcrafted environments in multiple cases. We also show how a re-interpretation of IRMv1 allows us for the first time to directly optimize a common fairness criterion, groupsufficiency, and thereby improve performance on a fair prediction task.

1. INTRODUCTION

Machine learning achieves super-human performance on many tasks when the test data is drawn from the same distribution as the training data. However, when the two distributions differ, model performance can severely degrade to even below chance predictions (Geirhos et al., 2020) . Tiny perturbations can derail classifiers, as shown by adversarial examples (Szegedy et al., 2014) and common-corruptions in image classification (Hendrycks & Dietterich, 2019) . Even new test sets collected from the same data acquisition pipeline induce distribution shifts that significantly harm performance (Recht et al., 2019; Engstrom et al., 2020) . Many approaches have been proposed to overcome model brittleness in the face of input distribution changes. Robust optimization aims to achieve good performance on any distribution close to the training distribution (Goodfellow et al., 2015; Duchi et al., 2016; Madry et al., 2018) . Domain generalization on the other hand tries to go one step further, to generalize to distributions potentially far away from the training distribution. The field of algorithmic fairness meanwhile primarily focuses on developing metrics to track and mitigate performance differences between different sub-populations or across similar individuals (Dwork et al., 2012; Corbett-Davies & Goel, 2018; Chouldechova & Roth, 2018) . Like domain generalization, evaluation using data related to but distinct from the training set is needed to characterize model failure. These evaluations are curated through the design of audits, which play a central role in revealing unfair algorithmic decision making (Buolamwini & Gebru, 2018; Obermeyer et al., 2019) . While the ultimate goals of domain generalization and algorithmic fairness are closely aligned, little research has focused on their similarities and how they can inform each other constructively. One of their main common goals can be characterized as: Learning algorithms robust to changes across domains or population groups.

