As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

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 …

Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …

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 …

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 …

Many existing fairness criteria for machine learning involve equalizing some metric across
protected groups such as race or gender. However, practitioners trying to audit or enforce …

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 …

Real-world datasets are often biased with respect to key demographic factors such as race
and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias …

Motivated by practical generalizations of the classic k-median and k-means objectives, such
as clustering with size constraints, fair clustering, and Wasserstein barycenter, we introduce …