Bias on Demand: When Synthetic Data Exposes the Moral Logic of AI Fairness
The audit starts badly when everyone asks for “the fairness metric” Audit. That is where many AI fairness conversations become prematurely tidy. A model has produced uneven outcomes. Someone asks whether it is “fair.” Someone else proposes demographic parity, equal opportunity, calibration, predictive parity, or whatever metric most recently escaped from a conference paper into a compliance slide. The room nods gravely. A dashboard is born. Justice, apparently, has been converted into a ratio. ...