Fairness in Streaming Submodular Maximization: Algorithms and Hardness

TL;DR

This paper introduces the first streaming algorithms for fair submodular maximization, enabling the creation of fair summaries in large datasets without significantly sacrificing utility.

Contribution

It develops novel streaming approximation algorithms for fair submodular maximization applicable to both monotone and non-monotone functions.

Findings

Fairness constraints do not significantly reduce utility.

Algorithms effectively handle large-scale datasets.

Validated on clustering, recommendation, summarization, and social networks.

Abstract

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representation of particular groups. This has made the design of fair machine learning algorithms increasingly important. In this work we address the question: Is it possible to create fair summaries for massive datasets? To this end, we develop the first streaming approximation algorithms for submodular maximization under fairness constraints, for both monotone and non-monotone functions. We validate our findings empirically on exemplar-based clustering, movie recommendation, DPP-based summarization, and maximum coverage in social networks, showing that fairness constraints do not significantly impact utility.