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Fair Soft Clustering

Rune D. Kj√¶rsgaard · Pekka Parviainen · Saket Saurabh · MADHUMITA KUNDU · Line Clemmensen

MR1 & MR2 - Number 37
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Thu 2 May 8 a.m. PDT — 8:30 a.m. PDT


Scholars in the machine learning community have recently focused on analyzing the fairness of learning models, including clustering algorithms. In this work we study fair clustering in a probabilistic (soft) setting, where observations may belong to several clusters determined by probabilities. We introduce new probabilistic fairness metrics, which generalize and extend existing non-probabilistic fairness frameworks and propose an algorithm for obtaining a fair probabilistic cluster solution from a data representation known as a fairlet decomposition. Finally, we demonstrate our proposed fairness metrics and algorithm by constructing a fair Gaussian mixture model on three real-world datasets. We achieve this by identifying balanced micro-clusters which minimize the distances induced by the model, and on which traditional clustering can be performed while ensuring the fairness of the solution.

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