Robust Fair Clustering with Group Membership Uncertainty Sets

Sharmila Duppala, Juan Luque, John P Dickerson, Seyed A. Esmaeili
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:2350-2358, 2025.

Abstract

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 the assigned group memberships are noisy. We introduce a simple noise model that requires a small number of parameters to be given by the decision maker. We then present an algorithm for fair clustering with provable \emph{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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-duppala25a, title = {Robust Fair Clustering with Group Membership Uncertainty Sets}, author = {Duppala, Sharmila and Luque, Juan and Dickerson, John P and Esmaeili, Seyed A.}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {2350--2358}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/duppala25a/duppala25a.pdf}, url = {https://proceedings.mlr.press/v258/duppala25a.html}, abstract = {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 the assigned group memberships are noisy. We introduce a simple noise model that requires a small number of parameters to be given by the decision maker. We then present an algorithm for fair clustering with provable \emph{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.} }
Endnote
%0 Conference Paper %T Robust Fair Clustering with Group Membership Uncertainty Sets %A Sharmila Duppala %A Juan Luque %A John P Dickerson %A Seyed A. Esmaeili %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-duppala25a %I PMLR %P 2350--2358 %U https://proceedings.mlr.press/v258/duppala25a.html %V 258 %X 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 the assigned group memberships are noisy. We introduce a simple noise model that requires a small number of parameters to be given by the decision maker. We then present an algorithm for fair clustering with provable \emph{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.
APA
Duppala, S., Luque, J., Dickerson, J.P. & Esmaeili, S.A.. (2025). Robust Fair Clustering with Group Membership Uncertainty Sets. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:2350-2358 Available from https://proceedings.mlr.press/v258/duppala25a.html.

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