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Exploring Rawlsian Fairness for K-Means Clustering. (arXiv:2205.02052v1 [cs.LG])
May 5, 2022, 1:12 a.m. | Stanley Simoes, Deepak P, Muiris MacCarthaigh
cs.LG updates on arXiv.org arxiv.org
We conduct an exploratory study that looks at incorporating John Rawls' ideas
on fairness into existing unsupervised machine learning algorithms. Our focus
is on the task of clustering, specifically the k-means clustering algorithm. To
the best of our knowledge, this is the first work that uses Rawlsian ideas in
clustering. Towards this, we attempt to develop a postprocessing technique
i.e., one that operates on the cluster assignment generated by the standard
k-means clustering algorithm. Our technique perturbs this assignment over …
More from arxiv.org / cs.LG updates on arXiv.org
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