UniFair: A unified fair clustering approach based on separation and compactness

ArXi:2606.04777v1 Announce Type: new Clustering is increasingly used to high-impact decisions, yet standard objectives such as $k$-means can produce clusterings that treat graphic groups unequally. Existing fair clustering methods typically optimize a single notion of fairness and often overlook how clustering costs interact with the geometry of the induced decision boundaries. We propose \textsc{UniFair}, a unified framework that jointly optimizes \emph{separation fairness} and \emph{social fairness.