SLIDE: a surrogate fairness constraint to ensure fairness consistency
This work addresses fairness in AI for social decision-making, offering an incremental improvement over existing surrogate constraints.
The paper tackles the problem of ensuring fairness in AI algorithms by proposing SLIDE, a new surrogate fairness constraint that is computationally feasible and asymptotically valid, achieving a fast convergence rate and performing well on various benchmark datasets.
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.