  Algorithmic fairness has emerged as an important consideration when using
machine learning to make high-stakes societal decisions. Yet, improved fairness
often comes at the expense of model accuracy. While aspects of the
fairness-accuracy tradeoff have been studied, most work reports the fairness
and accuracy of various models separately; this makes model comparisons nearly
impossible without a model-agnostic metric that reflects the balance of the two
desiderata. We seek to identify, quantify, and optimize the empirical Pareto
frontier of the fairness-accuracy tradeoff. Specifically, we identify and
outline the empirical Pareto frontier through
Tradeoff-between-Fairness-and-Accuracy (TAF) Curves; we then develop a metric
to quantify this Pareto frontier through the weighted area under the TAF Curve
which we term the Fairness-Area-Under-the-Curve (FAUC). TAF Curves provide the
first empirical, model-agnostic characterization of the Pareto frontier, while
FAUC provides the first metric to impartially compare model families on both
fairness and accuracy. Both TAF Curves and FAUC can be employed with all group
fairness definitions and accuracy measures. Next, we ask: Is it possible to
expand the empirical Pareto frontier and thus improve the FAUC for a given
collection of fitted models? We answer affirmately by developing a novel fair
model stacking framework, FairStacks, that solves a convex program to maximize
the accuracy of model ensemble subject to a score-bias constraint. We show that
optimizing with FairStacks always expands the empirical Pareto frontier and
improves the FAUC; we additionally study other theoretical properties of our
proposed approach. Finally, we empirically validate TAF, FAUC, and FairStacks
through studies on several real benchmark data sets, showing that FairStacks
leads to major improvements in FAUC that outperform existing algorithmic
fairness approaches.

Machine Learning;Computers and Society;Machine Learning;Methodology