  The potential for learned models to amplify existing societal biases has been
broadly recognized. Fairness-aware classifier constraints, which apply equality
metrics of performance across subgroups defined on sensitive attributes such as
race and gender, seek to rectify inequity but can yield non-uniform degradation
in performance for skewed datasets. In certain domains, imbalanced degradation
of performance can yield another form of unintentional bias. In the spirit of
constructing fairness-aware algorithms as societal imperative, we explore an
alternative: Pareto-Efficient Fairness (PEF). Theoretically, we prove that PEF
identifies the operating point on the Pareto curve of subgroup performances
closest to the fairness hyperplane, maximizing multiple subgroup accuracy.
Empirically we demonstrate that PEF outperforms by achieving Pareto levels in
accuracy for all subgroups compared to strict fairness constraints in several
UCI datasets.
