Bias on Demand: When Synthetic Data Exposes the Moral Logic of AI Fairness
Bias on Demand: When Synthetic Data Exposes the Moral Logic of AI Fairness In the field of machine learning, fairness is often treated as a technical constraint — a line of code to be added, a metric to be optimized. But behind every fairness metric lies a moral stance: what should be equalized, for whom, and at what cost? The paper “Bias on Demand: A Modelling Framework that Generates Synthetic Data with Bias” (Baumann et al., FAccT 2023) breaks this technical illusion by offering a framework that can manufacture bias in data — deliberately, transparently, and with philosophical intent. ...