{"ID":3050078,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T11:59:53.540122282Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04777","arxiv_id":"2606.04777","title":"UniFair: A unified fair clustering approach based on separation and compactness","abstract":"Clustering is increasingly used to support high-impact decisions, yet standard objectives such as $k$-means can produce clusterings that treat demographic 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}. Separation fairness encourages protected groups to lie farther from the induced decision boundaries, while social fairness reduces disparities in within-cluster distortion by penalizing group-wise clustering costs. We develop gradient-based optimization procedures for separation-fair and unified $k$-means objectives, and extend them to deep clustering by enforcing the same criteria in the latent space of an autoencoder. Experiments on tabular and image datasets show that \\textsc{UniFair} reduces both boundary-related and cost-based group disparities with only a modest increase in clustering loss.","short_abstract":"Clustering is increasingly used to support high-impact decisions, yet standard objectives such as $k$-means can produce clusterings that treat demographic groups unequally. Existing fair clustering methods typically optimize a single notion of fairness and often overlook how clustering costs interact with the geometry...","url_abs":"https://arxiv.org/abs/2606.04777","url_pdf":"https://arxiv.org/pdf/2606.04777v1","authors":"[\"Antonia Karra\",\"Vasiliki Papanikou\",\"Georgios Vardakas\",\"Evaggelia Pitoura\",\"Aristidis Likas\"]","published":"2026-06-03T12:00:22Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}