Proportionally Representative Clustering
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In recent years, there has been a surge in effort to formalize notions of fairness in machine learning. We focus on clustering – one of the fundamental tasks in unsupervised machine learning. We propose a new axiom that captures proportional representation fairness (PRF). We make a case that the concept achieves the raison d'être of several existing concepts in the literature in an arguably more convincing manner. Our fairness concept is not satisfied by existing fair clustering algorithms. We design efficient algorithms to achieve PRF both for unconstrained and discrete clustering problems.
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