  Fueled by massive data, important decision making is being automated with the
help of algorithms, therefore, fairness in algorithms has become an especially
important research topic. In this work, we design new streaming and distributed
algorithms for the fair $k$-center problem that models fair data summarization.
The streaming and distributed models of computation have an attractive feature
of being able to handle massive data sets that do not fit into main memory. Our
main contributions are: (a) the first distributed algorithm; which has provably
constant approximation ratio and is extremely parallelizable, and (b) a
two-pass streaming algorithm with a provable approximation guarantee matching
the best known algorithm (which is not a streaming algorithm). Our algorithms
have the advantages of being easy to implement in practice, being fast with
linear running times, having very small working memory and communication, and
outperforming existing algorithms on several real and synthetic data sets. To
complement our distributed algorithm, we also give a hardness result for
natural distributed algorithms, which holds for even the special case of
$k$-center.
