  We propose a distributionally robust support vector machine with a fairness
constraint that encourages the classifier to be fair in view of the equality of
opportunity criterion. We use a type-$\infty$ Wasserstein ambiguity set
centered at the empirical distribution to model distributional uncertainty and
derive an exact reformulation for worst-case unfairness measure. We establish
that the model is equivalent to a mixed-binary optimization problem, which can
be solved by standard off-the-shelf solvers. We further prove that the
expectation of the hinge loss objective function constitutes an upper bound on
the misclassification probability. Finally, we numerically demonstrate that our
proposed approach improves fairness with negligible loss of predictive
accuracy.
