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

arXiv:2602.07285v2 Announce Type: replace Abstract: 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. However, this does not extend to sufficiency: even perfectly group-calibrated scores -- including true class pro

Source: arXiv cs.LG — read the full report at the original publisher.

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