When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

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 fair variant of the classic k-median problem introduced by (Chierichetti et al.,
NeurIPS 2017) in which the points are colored, and the goal is to minimize the same …

Many clustering algorithms lead to cluster assignments that are hard to explain, partially
because they depend on all the features of the data in a complicated way. To improve …

In data summarization we want to choose $ k $ prototypes in order to summarize a data set.
We study a setting where the data set comprises several demographic groups and we are …

Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we
study a version of constrained SC in which we try to incorporate the fairness notion …

Given a set of $ n $ points in $ d $ dimensions, the Euclidean $ k $-means problem (resp.
Euclidean $ k $-median) consists of finding $ k $ centers such that the sum of squared …