Feb. 15, 2024, 5:42 a.m. | Yiqi Liu, Francesca Molinari

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08879v1 Announce Type: cross
Abstract: Decision-making processes increasingly rely on the use of algorithms. Yet, algorithms' predictive ability frequently exhibit systematic variation across subgroups of the population. While both fairness and accuracy are desirable properties of an algorithm, they often come at the cost of one another. What should a fairness-minded policymaker do then, when confronted with finite data? In this paper, we provide a consistent estimator for a theoretical fairness-accuracy frontier put forward by Liang, Lu and Mu (2023) …

abstract accuracy algorithm algorithmic fairness algorithms arxiv cost cs.lg decision econ.em fairness inference making population predictive processes subgroups type variation

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