Fair learning with Wasserstein barycenters for non-decomposable performance measures. (arXiv:2209.00427v1 [stat.ML])

This work provides several fundamental characterizations of the optimal
classification function under the demographic parity constraint. In the
awareness framework, akin to the classical unconstrained classification case,
we show that maximizing accuracy under this fairness constraint is equivalent
to solving a corresponding regression problem followed by thresholding at level
$1/2$. We extend this result to linear-fractional classification measures
(e.g., ${\rm F}$-score, AM measure, balanced accuracy, etc.), highlighting the
fundamental role played by the regression problem in this framework. Our
results …

arxiv learning performance