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Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. (arXiv:2210.01953v1 [cs.LG])
Oct. 6, 2022, 1:20 a.m. | Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu
cs.CR updates on arXiv.org arxiv.org
Clustering algorithms are widely used in many societal resource allocation
applications, such as loan approvals and candidate recruitment, among others,
and hence, biased or unfair model outputs can adversely impact individuals that
rely on these applications. To this end, many fair clustering approaches have
been recently proposed to counteract this issue. Due to the potential for
significant harm, it is essential to ensure that fair clustering algorithms
provide consistently fair outputs even under adversarial influence. However,
fair clustering algorithms have …
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