  Cohort Shapley value is a model-free method of variable importance grounded
in game theory that does not use any unobserved and potentially impossible
feature combinations. We use it to evaluate algorithmic fairness, using the
well known COMPAS recidivism data as our example. This approach allows one to
identify for each individual in a data set the extent to which they were
adversely or beneficially affected by their value of a protected attribute such
as their race. The method can do this even if race was not one of the original
predictors and even if it does not have access to a proprietary algorithm that
has made the predictions. The grounding in game theory lets us define aggregate
variable importance for a data set consistently with its per subject
definitions. We can investigate variable importance for multiple quantities of
interest in the fairness literature including false positive predictions.
