  Supervised learning systems are trained using historical data and, if the
data was tainted by discrimination, they may unintentionally learn to
discriminate against protected groups. We propose that fair learning methods,
despite training on potentially discriminatory datasets, shall perform well on
fair test datasets. Such dataset shifts crystallize application scenarios for
specific fair learning methods. For instance, the removal of direct
discrimination can be represented as a particular dataset shift problem. For
this scenario, we propose a learning method that provably minimizes model error
on fair datasets, while blindly training on datasets poisoned with direct
additive discrimination. The method is compatible with existing legal systems
and provides a solution to the widely discussed issue of protected groups'
intersectionality by striking a balance between the protected groups.
Technically, the method applies probabilistic interventions, has causal and
counterfactual formulations, and is computationally lightweight - it can be
used with any supervised learning model to prevent discrimination via proxies
while maximizing model accuracy for business necessity.
