Robust Fair Clustering with Group Membership Uncertainty Sets
CoRR(2024)
摘要
We study the canonical fair clustering problem where each cluster is
constrained to have close to population-level representation of each group.
Despite significant attention, the salient issue of having incomplete knowledge
about the group membership of each point has been superficially addressed. In
this paper, we consider a setting where errors exist in the assigned group
memberships. We introduce a simple and interpretable family of error models
that require a small number of parameters to be given by the decision maker. We
then present an algorithm for fair clustering with provable robustness
guarantees. Our framework enables the decision maker to trade off between the
robustness and the clustering quality. Unlike previous work, our algorithms are
backed by worst-case theoretical guarantees. Finally, we empirically verify the
performance of our algorithm on real world datasets and show its superior
performance over existing baselines.
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