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Poster
Guarantees for Spectral Clustering with Fairness Constraints
Matthäus Kleindessner · Samira Samadi · Pranjal Awasthi · Jamie Morgenstern

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #195
in Posters Thu »
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017). According to this notion, a clustering is fair if every demographic group is approximately proportionally represented in each cluster. To this end, we develop variants of both normalized and unnormalized constrained SC and show that they help find fairer clusterings on both synthetic and real data. We also provide a rigorous theoretical analysis of our algorithms on a natural variant of the stochastic block model, where $h$ groups have strong inter-group connectivity, but also exhibit a “natural” clustering structure which is fair. We prove that our algorithms can recover this fair clustering with high probability.
Keywords: Clustering · Fairness · Unsupervised Learning

Author Information

Matthäus Kleindessner (Rutgers University)
Samira Samadi (Georgia Tech)
Pranjal Awasthi (Rutgers University)
Jamie Morgenstern (Georgia Institute of Technology)

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