  Recent work has emphasized the importance of balancing competing objectives
in model training (e.g., accuracy vs. fairness, or competing measures of
fairness). Such trade-offs reflect a broader class of multi-objective
optimization (MOO) problems in which optimization methods seek Pareto optimal
trade-offs between competing goals. In this work, we first introduce a
differentiable measure that enables direct optimization of group fairness
(specifically, balancing accuracy across groups) in model training. Next, we
demonstrate two model-agnostic MOO frameworks for learning Pareto optimal
parameterizations over different groups of neural classification models. We
evaluate our methods on the specific task of hate speech detection, in which
prior work has shown lack of group fairness across speakers of different
English dialects. Empirical results across convolutional, sequential, and
transformer-based neural architectures show superior empirical accuracy vs.
fairness trade-offs over prior work. More significantly, our measure enables
the Pareto machinery to ensure that each architecture achieves the best
possible trade-off between fairness and accuracy w.r.t. the dataset, given
user-prescribed error tolerance bounds.
