  Predictive models are increasingly deployed for the purpose of determining
access to services such as credit, insurance, and employment. Despite potential
gains in productivity and efficiency, several potential problems have yet to be
addressed, particularly the potential for unintentional discrimination. We
present an iterative procedure, based on orthogonal projection of input
attributes, for enabling interpretability of black-box predictive models.
Through our iterative procedure, one can quantify the relative dependence of a
black-box model on its input attributes.The relative significance of the inputs
to a predictive model can then be used to assess the fairness (or
discriminatory extent) of such a model.
