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

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 (stati