Fair and Diverse DPP-based Data Summarization
This addresses fairness issues in data summarization for applications like dataset curation, though it is incremental as it builds on existing DPP methods.
The paper tackles the problem of bias in diverse data summarization by introducing a framework to incorporate fairness constraints into determinantal point processes (DPPs), with experimental results showing that diversity remains close to the unconstrained case.
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 (under- or over-representation of a certain gender or race) 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. Coming up with efficient algorithms to sample from these constrained determinantal distributions, however, suffers from a complexity barrier and we present a fast sampler that is provably good when the input vectors satisfy a natural property. Our experimental results on a real-world and an image dataset show that the diversity of the samples produced by adding fairness constraints is not too far from the unconstrained case, and we also provide a theoretical explanation of it.