  Current fairness toolkits in machine learning 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, flexible 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.
