Optimal Constrained Functional Prediction with Applications in Fair Machine Learning

The core message of this article is that constrained statistical learning problems, such as those arising in the context of algorithmic fairness, can be characterized as the estimation of a constrained functional parameter. The authors develop a general framework for deriving closed-form solutions to these constrained optimization problems and propose model-agnostic estimators that can be integrated with standard statistical learning approaches.