On Learning Fairness and Accuracy on Multiple Subgroups. (arXiv:2210.10837v1 [stat.ML])

We propose an analysis in fair learning that preserves the utility of the
data while reducing prediction disparities under the criteria of group
sufficiency. We focus on the scenario where the data contains multiple or even
many subgroups, each with limited number of samples. As a result, we present a
principled method for learning a fair predictor for all subgroups via
formulating it as a bilevel objective. Specifically, the subgroup specific
predictors are learned in the lower-level through a small …

accuracy arxiv fairness subgroups