  As AI systems develop in complexity it is becoming increasingly hard to
ensure non-discrimination on the basis of protected attributes such as gender,
age, and race. Many recent methods have been developed for dealing with this
issue as long as the protected attribute is explicitly available for the
algorithm. We address the setting where this is not the case (with either no
explicit protected attribute, or a large set of them). Instead, we assume the
existence of a fair domain expert capable of generating an extension to the
labeled dataset - a small set of example pairs, each having a different value
on a subset of protected variables, but judged to warrant a similar model
response. We define a performance metric - paired consistency. Paired
consistency measures how close the output (assigned by a classifier or a
regressor) is on these carefully selected pairs of examples for which fairness
dictates identical decisions. In some cases consistency can be embedded within
the loss function during optimization and serve as a fairness regularizer, and
in others it is a tool for fair model selection. We demonstrate our method
using the well studied Income Census dataset.
