Online Augmented Plug-in Inference for Multi-group Fair Prediction

Abstract

Fairness regularization changes both a prediction rule and its sampling uncertainty. We develop online inference for classifiers trained with a covariance penalty on group mean decision scores, a tractable proxy for demographic parity. The framework includes logistic regression and smoothed support-vector machines, with linear regression as a special case targeting equality of group mean predictions. The inferential challenge arises because the fairness operator is estimated from the same stream used to update the predictor. Each observation contributes to its current loss gradient and to subsequent penalty estimates, creating an additional first-order fluctuation in the Polyak--Ruppert average. We derive an asymptotic expansion with harmonic reuse weights and an augmented covariance incorporating score variation, operator variation, and their dependence. Exact fixed-memory recursions yield a consistent covariance estimator and asymptotically valid confidence intervals for the penalized coefficients. Simulation studies assess coverage and interval contraction. Adult-income classification and IPUMS wage regression illustrate coefficient uncertainty and fairness--utility trade-offs, with classification demographic parity evaluated separately through threshold decisions.