  Sampling methods that choose a subset of the data proportional to its
diversity in the feature space are popular for data summarization. However,
recent studies have noted the occurrence of bias (under- or over-representation
of a certain gender or race) in such data summarization methods. In this paper
we initiate a study of the problem of outputting a diverse and fair summary of
a given dataset. We work with a well-studied determinantal measure of diversity
and corresponding distributions (DPPs) and present a framework that allows us
to incorporate a general class of fairness constraints into such distributions.
Coming up with efficient algorithms to sample from these constrained
determinantal distributions, however, suffers from a complexity barrier and we
present a fast sampler that is provably good when the input vectors satisfy a
natural property. Our experimental results on a real-world and an image dataset
show that the diversity of the samples produced by adding fairness constraints
is not too far from the unconstrained case, and we also provide a theoretical
explanation of it.
