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Offline Contextual Bandits with High Probability Fairness Guarantees
Blossom Metevier · Stephen Giguere · Sarah Brockman · Ari Kobren · Yuriy Brun · Emma Brunskill · Philip Thomas

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #113

We present RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints. Our algorithm accepts multiple fairness definitions and allows users to construct their own unique fairness definitions for the problem at hand. We provide a theoretical analysis of RobinHood, which includes a proof that it will not return an unfair solution with probability greater than a user-specified threshold. We validate our algorithm on three applications: a user study with an automated tutoring system, a loan approval setting using the Statlog German credit data set, and a criminal recidivism problem using data released by ProPublica. To demonstrate the versatility of our approach, we use multiple well-known and custom definitions of fairness. In each setting, our algorithm is able to produce fair policies that achieve performance competitive with other offline and online contextual bandit algorithms.

Author Information

Blossom Metevier (University of Massachusetts, Amherst)
Stephen Giguere (University of Massachusetts, Amherst)
Sarah Brockman (University of Massachusetts Amherst)
Ari Kobren (UMass Amherst)
Yuriy Brun (University of Massachusetts Amherst)
Emma Brunskill (Stanford University)
Philip Thomas (University of Massachusetts Amherst)

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