fairnessconstrained
Fairnessconstrained is a term used in the field of machine learning and decision theory to describe approaches that enforce fairness through explicit constraints in the optimization process. A fairness-constrained model aims to maximize predictive accuracy or utility while ensuring that outcomes do not disproportionately favor or disadvantage members of protected groups defined by attributes such as race, gender, or age. The concept encompasses both the formulation of constraints and the algorithms used to satisfy them during training, deployment, or post-processing.
Common formulations introduce constraints into the objective function or into the decision rule. Typical fairness constraints
Applications appear in lending, hiring, college admissions, and criminal justice risk assessments, among others. While fairness
Fairnessconstrained sits at the intersection of machine learning, ethics, and policy, and is one among several