  Diversity is an important principle in data selection and summarization,
facility location, and recommendation systems. Our work focuses on maximizing
diversity in data selection, while offering fairness guarantees. In particular,
we offer the first study that augments the Max-Min diversification objective
with fairness constraints. More specifically, given a universe $U$ of $n$
elements that can be partitioned into $m$ disjoint groups, we aim to retrieve a
$k$-sized subset that maximizes the pairwise minimum distance within the set
(diversity) and contains a pre-specified $k_i$ number of elements from each
group $i$ (fairness). We show that this problem is NP-complete even in metric
spaces, and we propose three novel algorithms, linear in $n$, that provide
strong theoretical approximation guarantees for different values of $m$ and
$k$. Finally, we extend our algorithms and analysis to the case where groups
can be overlapping.
