TL;DR for operators Fairness work usually arrives in one of two flavours: mathematical fog or compliance theatre. OptFair is more useful than both. Zhang et al. study multi-class fair classification and show how to define the optimal accuracy-fairness frontier, then approximate it through two deployable routes: intervention during training and calibration after training.1 ...
A hiring classifier works beautifully on average. A content moderation model passes global accuracy tests. A medical image model looks reassuringly competent across the validation set. Then someone asks the annoying question every serious deployment eventually faces: which group does it fail on? That is where average accuracy starts behaving like a corporate dashboard after a long lunch: technically present, emotionally comforting, and not especially interested in the unpleasant details. ...