  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.
