UniFair: A unified fair clustering approach based on separation and compactness
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 UniFair, a unified framework that jointly optimizes separation fairness and 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-base...
| Authors | Antonia Karra, Vasiliki Papanikou, Georgios Vardakas et al. |
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| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 4 June 2026 |
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