  We present an $(e^{O(p)} \frac{\log \ell}{\log\log\ell})$-approximation
algorithm for socially fair clustering with the $\ell_p$-objective. In this
problem, we are given a set of points in a metric space. Each point belongs to
one (or several) of $\ell$ groups. The goal is to find a $k$-medians,
$k$-means, or, more generally, $\ell_p$-clustering that is simultaneously good
for all of the groups. More precisely, we need to find a set of $k$ centers $C$
so as to minimize the maximum over all groups $j$ of $\sum_{u \text{ in group
}j} d(u,C)^p$. The socially fair clustering problem was independently proposed
by Abbasi, Bhaskara, and Venkatasubramanian [2021] and Ghadiri, Samadi, and
Vempala [2021]. Our algorithm improves and generalizes their
$O(\ell)$-approximation algorithms for the problem. The natural LP relaxation
for the problem has an integrality gap of $\Omega(\ell)$. In order to obtain
our result, we introduce a strengthened LP relaxation and show that it has an
integrality gap of $\Theta(\frac{\log \ell}{\log\log\ell})$ for a fixed $p$.
Additionally, we present a bicriteria approximation algorithm, which
generalizes the bicriteria approximation of Abbasi et al. [2021].
