Regression under demographic parity constraints via unlabeled post-processing
This addresses fairness in machine learning for regression tasks, particularly when sensitive attributes are unavailable at inference time, though it appears incremental as a post-processing method.
The paper tackles the problem of performing regression while ensuring demographic parity without access to sensitive attributes during inference, presenting a post-processing algorithm that generates predictions meeting this constraint with theoretical guarantees and experimental validation.
We address the problem of performing regression while ensuring demographic parity, even without access to sensitive attributes during inference. We present a general-purpose post-processing algorithm that, using accurate estimates of the regression function and a sensitive attribute predictor, generates predictions that meet the demographic parity constraint. Our method involves discretization and stochastic minimization of a smooth convex function. It is suitable for online post-processing and multi-class classification tasks only involving unlabeled data for the post-processing. Unlike prior methods, our approach is fully theory-driven. We require precise control over the gradient norm of the convex function, and thus, we rely on more advanced techniques than standard stochastic gradient descent. Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings.