Attraction-Repulsion clustering with applications to fairness
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In the framework of fair learning, we consider clustering methods that avoid or limit the influence of a set of protected attributes, S, (race, sex, etc) over the resulting clusters, with the goal of producing a fair clustering. For this, we introduce perturbations to the Euclidean distance that take into account S in a way that resembles attraction-repulsion in charged particles in Physics and results in dissimilarities with an easy interpretation. Cluster analysis based on these dissimilarities penalizes homogeneity of the clusters in the attributes S, and leads to an improvement in fairness. We illustrate the use of our procedures with both synthetic and real data.
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