Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making. (arXiv:2205.04790v1 [stat.ML])

Web: http://arxiv.org/abs/2205.04790

Decision making algorithms, in practice, are often trained on data that
exhibits a variety of biases. Decision-makers often aim to take decisions based
on some ground-truth target that is assumed or expected to be unbiased, i.e.,
equally distributed across socially salient groups. In many practical settings,
the ground-truth cannot be directly observed, and instead, we have to rely on a
biased proxy measure of the ground-truth, i.e., biased labels, in the data. In
addition, data is often selectively labeled, i.e., …

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