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Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection

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.

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