STOCHASTIC DIFFERENTIALLY PRIVATE AND FAIR LEARNING

Abstract

Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learning algorithms have been developed to mitigate discrimination issues, these algorithms can still leak sensitive information, such as individuals' health or financial records. Utilizing the notion of differential privacy (DP), prior works aimed at developing learning algorithms that are both private and fair. However, existing algorithms for DP fair learning are either not guaranteed to converge or require full batch of data in each iteration of the algorithm to converge. In this paper, we provide the first stochastic differentially private algorithm for fair learning that is guaranteed to converge. Here, the term "stochastic" refers to the fact that our proposed algorithm converges even when minibatches of data are used at each iteration (i.e. stochastic optimization). Our framework is flexible enough to permit different fairness notions, including demographic parity and equalized odds. In addition, our algorithm can be applied to non-binary classification tasks with multiple (non-binary) sensitive attributes. As a byproduct of our convergence analysis, we provide the first utility guarantee for a DP algorithm for solving nonconvex-strongly concave min-max problems. Our numerical experiments show that the proposed algorithm consistently offers significant performance gains over the state-of-the-art baselines, and can be applied to larger scale problems with non-binary target/sensitive attributes.

1. INTRODUCTION

In recent years, machine learning algorithms have been increasingly used to inform decisions with far-reaching consequences (e.g. whether to release someone from prison or grant them a loan), raising concerns about their compliance with laws, regulations, societal norms, and ethical values. Specifically, machine learning algorithms have been found to discriminate against certain "sensitive" demographic groups (e.g. racial minorities), prompting a profusion of algorithmic fairness research (Dwork et al., 2012; Sweeney, 2013; Datta et al., 2015; Feldman et al., 2015; Bolukbasi et al., 2016; Angwin et al., 2016; Calmon et al., 2017; Hardt et al., 2016a; Fish et al., 2016; Woodworth et al., 2017; Zafar et al., 2017; Bechavod & Ligett, 2017; Kearns et al., 2018; Prost et al., 2019; Baharlouei et al., 2020; Lowy et al., 2022a) . Algorithmic fairness literature aims to develop fair machine learning algorithms that output non-discriminatory predictions. Fair learning algorithms typically need access to the sensitive data in order to ensure that the trained model is non-discriminatory. However, consumer privacy laws (such as the E.U. General Data Protection Regulation) restrict the use of sensitive demographic data in algorithmic decision-making. ˚Work done as a visiting scholar at the University of Southern California, Viterbi School of Engineering.

