  As they have a vital effect on social decision makings, AI algorithms should
be not only accurate and but also fair. Among various algorithms for fairness
AI, learning a prediction model by minimizing the empirical risk (e.g.,
cross-entropy) subject to a given fairness constraint has received much
attention. To avoid computational difficulty, however, a given fairness
constraint is replaced by a surrogate fairness constraint as the 0-1 loss is
replaced by a convex surrogate loss for classification problems. In this paper,
we investigate the validity of existing surrogate fairness constraints and
propose a new surrogate fairness constraint called SLIDE, which is
computationally feasible and asymptotically valid in the sense that the learned
model satisfies the fairness constraint asymptotically and achieves a fast
convergence rate. Numerical experiments confirm that the SLIDE works well for
various benchmark datasets.
