  Data containing human or social attributes may over- or under-represent
groups with respect to salient social attributes such as gender or race, which
can lead to biases in downstream applications. This paper presents an
algorithmic framework that can be used as a data preprocessing method towards
mitigating such bias. Unlike prior work, it can efficiently learn distributions
over large domains, controllably adjust the representation rates of protected
groups and achieve target fairness metrics such as statistical parity, yet
remains close to the empirical distribution induced by the given dataset. Our
approach leverages the principle of maximum entropy - amongst all distributions
satisfying a given set of constraints, we should choose the one closest in
KL-divergence to a given prior. While maximum entropy distributions can
succinctly encode distributions over large domains, they can be difficult to
compute. Our main contribution is an instantiation of this framework for our
set of constraints and priors, which encode our bias mitigation goals, and that
runs in time polynomial in the dimension of the data. Empirically, we observe
that samples from the learned distribution have desired representation rates
and statistical rates, and when used for training a classifier incurs only a
slight loss in accuracy while maintaining fairness properties.
