Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk
  Minimization Framework
Sina Baharlouei Meisam Razaviyayn
While training fair machine learning models has been studied extensively in
recent years, most developed methods rely on the assumption that the training
and test data have similar distributions. In the presence of distribution
shifts, fair models may behave unfairly on test data. There have been some
developments for fair learning robust to distribution shifts to address this
shortcoming. However, most proposed solutions are based on the assumption of
having access to the causal graph describing the interaction of different
features. Moreover, existing algorithms require full access to data and cannot
be used when small batches are used (stochastic/batch implementation). This
paper proposes the first stochastic distributionally robust fairness framework
with convergence guarantees that do not require knowledge of the causal graph.
More specifically, we formulate the fair inference in the presence of the
distribution shift as a distributionally robust optimization problem under
$L_p$ norm uncertainty sets with respect to the Exponential Renyi Mutual
Information (ERMI) as the measure of fairness violation. We then discuss how
the proposed method can be implemented in a stochastic fashion. We have
evaluated the presented framework's performance and efficiency through
extensive experiments on real datasets consisting of distribution shifts.