Fairness in Streaming Submodular Maximization: Algorithms and Hardness

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.

arXiv commentAccepted to NeurIPS 2020

References in corpus (6)

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• The Frontiers of Fairness in Machine Learning
• Fair and Diverse DPP-based Data Summarization
• Do Less, Get More: Streaming Submodular Maximization with Subsampling
• Beyond $1/2$-Approximation for Submodular Maximization on Massive Data Streams
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Cited by in corpus (5)

• View-based Explanations for Graph Neural Networks
• Streaming Algorithms for Diversity Maximization with Fairness Constraints
• Fully Dynamic Submodular Maximization over Matroids
• Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms
• Fair Diversity Maximization with Few Representatives