Welfare-Centric Clustering
This work addresses fairness in clustering for applications like resource allocation, offering a novel approach that may improve outcomes over incremental methods.
The paper tackles the problem of fair clustering by shifting from traditional fairness notions to a welfare-centric approach that models group utilities based on distances and proportional representation, introducing algorithms for Rawlsian and Utilitarian objectives that significantly outperform existing baselines in empirical evaluations.
Fair clustering has traditionally focused on ensuring equitable group representation or equalizing group-specific clustering costs. However, Dickerson et al. (2025) recently showed that these fairness notions may yield undesirable or unintuitive clustering outcomes and advocated for a welfare-centric clustering approach that models the utilities of the groups. In this work, we model group utilities based on both distances and proportional representation and formalize two optimization objectives based on welfare-centric clustering: the Rawlsian (Egalitarian) objective and the Utilitarian objective. We introduce novel algorithms for both objectives and prove theoretical guarantees for them. Empirical evaluations on multiple real-world datasets demonstrate that our methods significantly outperform existing fair clustering baselines.