Current tools for machine learning fairness only admit a limited range of
fairness definitions and have seen little integration with automatic
differentiation libraries, despite the central role these libraries play in
modern machine learning pipelines.
We introduce a framework of fairness regularization terms (fairrets) which
quantify bias as modular objectives that are easily integrated in automatic
differentiation pipelines. By employing a general definition of fairness in
terms of linear-fractional statistics, a wide class of fairrets can be computed
efficiently. Experiments show the behavior of their gradients and their utility
in enforcing fairness with minimal loss of predictive power compared to
baselines. Our contribution includes a PyTorch implementation of the fairret
framework.

将公平性的定义量化为模块化目标，易于与自动微分流程集成的公平化正则化项 (fairrets) 框架，通过广义公平性的线性分数统计量定义，可以高效计算出多种 fairrets。实验证明其梯度的行为以及与基准方法相比在保持最小预测准确性损失的同时实施公平性的效果。我们的贡献包括 fairret 框架的 PyTorch 实现。