Learning Time-Varying Convexifications of Multiple Fairness Measures

Quan Zhou, Jakub Marecek, Robert Noel Shorten

Primary Area: societal considerations including fairness, safety, privacy

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Keywords: Fairness in Machine Learning, Multiple Fairness Measures, Graph-structured Bandits

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Abstract: There is an increasing appreciation that one may need to consider multiple measures of fairness, e.g., considering multiple group and individual fairness notions. The relative weights of the fairness regularisers are a priori unknown, may be time varying, and need to be learned on the fly. We consider the learning of time-varying convexifications of multiple fairness measures with traditional full-information feedback and with limited graph-structured feedback.

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Submission Number: 3567