  Maximizing a submodular function has a wide range of applications in machine
learning and data mining. One such application is data summarization whose goal
is to select a small set of representative and diverse data items from a large
dataset. However, data items might have sensitive attributes such as race or
gender, in this setting, it is important to design \emph{fairness-aware}
algorithms to mitigate potential algorithmic bias that may cause over- or
under- representation of particular groups. Motivated by that, we propose and
study the classic non-monotone submodular maximization problem subject to novel
group fairness constraints. Our goal is to select a set of items that maximizes
a non-monotone submodular function, while ensuring that the number of selected
items from each group is proportionate to its size, to the extent specified by
the decision maker. We develop the first constant-factor approximation
algorithms for this problem. We also extend the basic model to incorporate an
additional global size constraint on the total number of selected items.
