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 …

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 …

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 …

The fair-ranking problem, which asks to rank a given set of items to maximize utility subject
to group fairness constraints, has received attention in the fairness, information retrieval, and …

We consider two key issues faced by many clustering methods when used for data
summarization, namely (a) an unfair representation of" demographic groups” and (b) …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned
into clusters that consist of nearby points in a metric space. A recent variant, fair clustering …

Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data
Mining, to Machine Learning and Operations Research. However, in a variety of situations …

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …

Clustering is a fundamental problem in unsupervised machine learning, and due to its
numerous societal implications fair variants of it have recently received significant attention …