  Ensembling multiple Deep Neural Networks (DNNs) is a simple and effective way
to improve top-line metrics and to outperform a larger single model. In this
work, we go beyond top-line metrics and instead explore the impact of
ensembling on subgroup performances. Surprisingly, we observe that even with a
simple homogeneous ensemble -- all the individual DNNs share the same training
set, architecture, and design choices -- the minority group performance
disproportionately improves with the number of models compared to the majority
group, i.e. fairness naturally emerges from ensembling. Even more surprising,
we find that this gain keeps occurring even when a large number of models is
considered, e.g. $20$, despite the fact that the average performance of the
ensemble plateaus with fewer models. Our work establishes that simple DNN
ensembles can be a powerful tool for alleviating disparate impact from DNN
classifiers, thus curbing algorithmic harm. We also explore why this is the
case. We find that even in homogeneous ensembles, varying the sources of
stochasticity through parameter initialization, mini-batch sampling, and
data-augmentation realizations, results in different fairness outcomes.
