Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiency

ArXi:2602.07285v2 Announce Type: replace Binary classification based on predicted probabilities (scores) is a fundamental task in supervised machine learning. While thresholding scores is Bayes-optimal in the unconstrained setting, using a single threshold generally violates statistical group fairness constraints. Under independence (statistical parity) and separation (equalized odds), such thresholding suffices when the scores already satisfy the corresponding criterion.