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Conference Papers Year : 2023

Trading-off price for data quality to achieve fair online allocation

Abstract

We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the protected attributes -- which is often unrealistic in practice. Instead they can purchase data that help estimate them from sources of different quality; and hence reduce the fairness penalty at some cost. We model this problem as a multi-armed bandit problem where each arm corresponds to the choice of a data source, coupled with the online allocation problem. We propose an algorithm that jointly solves both problems and show that it has a regret bounded by $\mathcal{O}(\sqrt{T})$. A key difficulty is that the rewards received by selecting a source are correlated by the fairness penalty, which leads to a need for randomization (despite a stochastic setting). Our algorithm takes into account contextual information available before the source selection, and can adapt to many different fairness notions. We also show that in some instances, the estimates used can be learned on the fly.

Dates and versions

hal-04360656 , version 1 (21-12-2023)

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Mathieu Molina, Nicolas Gast, Patrick Loiseau, Vianney Perchet. Trading-off price for data quality to achieve fair online allocation. NeurIPS 2023 - 37th Conference on Neural Information Processing Systems, Dec 2023, New orleans, USA, United States. pp.1-43. ⟨hal-04360656⟩
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