Fairness in Learning: Classic and Contextual Bandits

NeurIPS 2016 Matthew JosephMichael KearnsJamie MorgensternAaron Roth

We introduce the study of fairness in multi-armed bandit problems. Our fairness definition can be interpreted as demanding that given a pool of applicants (say, for college admission or mortgages), a worse applicant is never favored over a better one, despite a learning algorithm's uncertainty over the true payoffs... (read more)

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