Fairness in Streaming Submodular Maximization over a Matroid Constraint

TL;DR

This paper introduces algorithms and theoretical insights for fair streaming submodular maximization under matroid constraints, addressing bias in large-scale data selection tasks with practical validation.

Contribution

It extends fair submodular maximization to matroid constraints, providing new algorithms and impossibility results for efficiency, quality, and fairness trade-offs.

Findings

Algorithms achieve fairness with acceptable efficiency and quality trade-offs.

Impossibility results highlight fundamental limits of fairness and performance.

Empirical validation on clustering, recommendation, and social network coverage.

Abstract

Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submodular maximization under a cardinality constraint. In this paper, we study the natural generalization of this problem to a matroid constraint. We give streaming algorithms as well as impossibility results that provide trade-offs between efficiency, quality and fairness. We validate our findings empirically on a range of well-known real-world applications: exemplar-based clustering, movie recommendation, and maximum coverage in social networks.