The fairness of machine learning-based decisions has become an increasingly important focus in the design of supervised machine learning methods. Most fairness approaches optimize a specified trade-off between performance measure(s) (e.g., accuracy, log loss, or AUC) and fairness metric(s) (e.g., demographic parity, equalized odds). This begs the question: are the right performance-fairness trade-offs being specified? We instead re-cast fair machine learning as an imitation learning task by introducing superhuman fairness, which seeks to simultaneously outperform human decisions on multiple predictive performance and fairness measures. We demonstrate the benefits of this approach given suboptimal decisions.

翻译：基于机器学习的决策公平性已成为监督学习方法设计中日益重要的关注焦点。大多数公平性方法通过优化性能指标（如准确率、对数损失或AUC）与公平性度量（如人口统计均等性、均等化几率）之间的特定权衡来实现。这引发了一个关键问题：我们是否设定了恰当的性能-公平性权衡？本文提出超人类公平这一概念，将公平机器学习重新定义为模仿学习任务，旨在同时在多项预测性能与公平性指标上超越人类决策水平。我们通过次优决策场景证明了该方法的有效性。