Spurious Minds: How Embedding Regularization Could Fix Bias at Its Roots
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. ...