Multiaccurate Proxies for Downstream Fairness

TL;DR

This paper introduces a method for training fair models without direct access to sensitive features by using multiaccuracy proxies learned from other attributes, ensuring fairness in downstream tasks.

Contribution

It proposes a novel fairness pipeline using multiaccuracy constraints to create proxies for sensitive features, with algorithms and bounds for effective learning.

Findings

Multiaccuracy proxies can be learned efficiently.

Proxies enable fair downstream models without sensitive feature access.

Multiaccuracy is easier to satisfy than classification accuracy.

Abstract

We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time -- in other words, how can we train a model to be fair by race when we don't have data about race? We adopt a fairness pipeline perspective, in which an "upstream" learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose, provide sample- and oracle efficient-algorithms and generalization bounds for learning such proxies, and conduct an experimental evaluation. In general, multiaccuracy is much easier to satisfy than classification accuracy, and can be satisfied even when the sensitive features are hard to predict.