FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee. (arXiv:2211.15072v3 [stat.ML] UPDATED)

Algorithmic fairness plays an increasingly critical role in machine learning
research. Several group fairness notions and algorithms have been proposed.
However, the fairness guarantee of existing fair classification methods mainly
depends on specific data distributional assumptions, often requiring large
sample sizes, and fairness could be violated when there is a modest number of
samples, which is often the case in practice. In this paper, we propose FaiREE,
a fair classification algorithm that can satisfy group fairness constraints
with finite-sample and …

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