In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

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 …

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 …

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 …

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 …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-
world applications such as computer vision and recommendations. For example, recognition …