  We consider training machine learning models that are fair in the sense that
their performance is invariant under certain sensitive perturbations to the
inputs. For example, the performance of a resume screening system should be
invariant under changes to the gender and/or ethnicity of the applicant. We
formalize this notion of algorithmic fairness as a variant of individual
fairness and develop a distributionally robust optimization approach to enforce
it during training. We also demonstrate the effectiveness of the approach on
two ML tasks that are susceptible to gender and racial biases.
