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Fair and Diverse DPP-Based Data Summarization
L. Elisa Celis · Vijay Keswani · Damian Straszak · Amit Jayant Deshpande · Tarun Kathuria · Nisheeth Vishnoi

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #111

Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias – e.g., under or over representation of a particular gender or ethnicity – in such data summarization methods. In this paper we initiate a study of the problem of outputting a diverse and fair summary of a given dataset. We work with a well-studied determinantal measure of diversity and corresponding distributions (DPPs) and present a framework that allows us to incorporate a general class of fairness constraints into such distributions. Designing efficient algorithms to sample from these constrained determinantal distributions, however, suffers from a complexity barrier; we present a fast sampler that is provably good when the input vectors satisfy a natural property. Our empirical results on both real-world and synthetic datasets show that the diversity of the samples produced by adding fairness constraints is not too far from the unconstrained case.

Author Information

L. Elisa Celis (Yale)
Vijay Keswani (EPFL)
Damian Straszak (EPFL)
Amit Jayant Deshpande (Microsoft Research)
Tarun Kathuria (UC Berkeley)
Nisheeth Vishnoi (EPFL)

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