We study the problem of finding low-cost {\em fair clusterings} in data where each data point
may belong to many protected groups. Our work significantly generalizes the seminal work …

We study the question of fair clustering under the {\em disparate impact} doctrine, where
each protected class must have approximately equal representation in every cluster. We …

Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and
may be used to make decisions for each point based on its group. However, this process …

In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …

We study data clustering problems with $\ell_p $-norm objectives (eg\textsc {$ k $-Median}
and\textsc {$ k $-Means}) in the context of individual fairness. The dataset consists of $ n …

What does it mean for a clustering to be fair? One popular approach seeks to ensure that
each cluster contains groups in (roughly) the same proportion in which they exist in the …

Clustering is a foundational problem in machine learning with numerous applications. As
machine learning increases in ubiquity as a backend for automated systems, concerns …

We extend the fair machine learning literature by considering the problem of proportional
centroid clustering in a metric context. For clustering n points with k centers, we define …

In this work, we study fairness in centroid clustering. In this problem, k cluster centers must
be placed given n points in a metric space, and the cost to each point is its distance to the …

We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety
of scientific data, can result in outcomes that are unfavorable to subgroups of data (eg …