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Poster
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Marwa El Halabi · Slobodan Mitrović · Ashkan Norouzi-Fard · Jakab Tardos · Jakub Tarnawski

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #513
in Poster Session 2 »

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.

Author Information

Marwa El Halabi (Samsung SAIT AI Lab Montreal)
Slobodan Mitrović (MIT)
Ashkan Norouzi-Fard (Google Research)
Jakab Tardos (EPFL)
Jakub Tarnawski (Microsoft Research)

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