2023-04-11-ahmadian23a.md

title	software	abstract	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Improved Approximation for Fair Correlation Clustering	https://github.com/moonin12/improved_fair_correlation_clustering/tree/main/lp_res_color_dist	Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge. Motivated by this, we study fair correlation clustering where the data points may belong to different protected groups and the goal is to ensure fair representation of all groups across clusters. Our paper significantly generalizes and improves on the quality guarantees of previous work of Ahmadian et al. as follows. * We allow the user to specify an arbitrary upper bound on the representation of each group in a cluster. * Our algorithm allows individuals to have multiple protected features and ensure fairness simultaneously across them all. * We prove guarantees for clustering quality and fairness in this general setting. Furthermore, this improves on the results for the special cases studied in previous work. Our experiments on real-world data demonstrate that our clustering quality compared to the optimal solution is much better than what our theoretical result suggests.	Regular Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ahmadian23a	0	Improved Approximation for Fair Correlation Clustering	9499	9516	9499-9516	9499	false	Ahmadian, Sara and Negahbani, Maryam	given family Sara Ahmadian given family Maryam Negahbani	2023-04-11		Proceedings of The 26th International Conference on Artificial Intelligence and Statistics	206	inproceedings	date-parts 2023 4 11	https://proceedings.mlr.press/v206/ahmadian23a/ahmadian23a.pdf