Differentially Private Fair Learning

Abstract

Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two learning algorithms that simultaneously promise differential privacy and equalized odds, a 'fairness' condition that corresponds to equalizing false positive and negative rates across protected groups. Our first algorithm is a private implementation of the equalized odds post-processing approach of [Hardt et al., 2016]. This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of [Agarwal et al., 2018] that can be used to find the optimal fair classifier, given access to a subroutine that can solve the original (not necessarily fair) learning problem. This algorithm is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.

References in corpus (4)

• Equality of Opportunity in Supervised Learning
• Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
• The Frontiers of Fairness in Machine Learning
• A Reductions Approach to Fair Classification

Cited by in corpus (12)

• A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
• Noise-tolerant fair classification
• On the Legal Compatibility of Fairness Definitions
• Fair Learning with Private Demographic Data
• Equity and Privacy: More Than Just a Tradeoff
• Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
• A Fairness Analysis on Private Aggregation of Teacher Ensembles
• Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
• Decision Making with Differential Privacy under a Fairness Lens
• Multi-Differential Fairness Auditor for Black Box Classifiers
• Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
• Near-Optimal Procedures for Model Discrimination with Non-Disclosure Properties