  Diversity maximization is a fundamental problem with wide applications in
data summarization, web search, and recommender systems. Given a set $X$ of $n$
elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum
\emph{diversity}, as quantified by the dissimilarities among the elements in
$S$. In this paper, we focus on the diversity maximization problem with
fairness constraints in the streaming setting. Specifically, we consider the
max-min diversity objective, which selects a subset $S$ that maximizes the
minimum distance (dissimilarity) between any pair of distinct elements within
it. Assuming that the set $X$ is partitioned into $m$ disjoint groups by some
sensitive attribute, e.g., sex or race, ensuring \emph{fairness} requires that
the selected subset $S$ contains $k_i$ elements from each group $i \in [1,m]$.
A streaming algorithm should process $X$ sequentially in one pass and return a
subset with maximum \emph{diversity} while guaranteeing the fairness
constraint. Although diversity maximization has been extensively studied, the
only known algorithms that can work with the max-min diversity objective and
fairness constraints are very inefficient for data streams. Since diversity
maximization is NP-hard in general, we propose two approximation algorithms for
fair diversity maximization in data streams, the first of which is
$\frac{1-\varepsilon}{4}$-approximate and specific for $m=2$, where
$\varepsilon \in (0,1)$, and the second of which achieves a
$\frac{1-\varepsilon}{3m+2}$-approximation for an arbitrary $m$. Experimental
results on real-world and synthetic datasets show that both algorithms provide
solutions of comparable quality to the state-of-the-art algorithms while
running several orders of magnitude faster in the streaming setting.
