Fair synthetic data is not about fairness

Abstract

Fair synthetic data (FSD) techniques aim to reduce algorithmic bias in AI prediction by generating artificial training datasets with idealised fairness properties. I argue that current FSD approaches fail to substantively improve fairness but increase the social autonomy of model owners, shielding them from accountability. I establish that implementing algorithmic fairness requires following normative commitments and accepting real-world sacrifices beyond technical trade-offs. I reframe synthetic training data sui generis as a domain-agnostic technique for improving model performance and demonstrate that synthetic data researchers treat fairness as a problem with data rather than the normative problem it is. I reconstruct three justifications for the substantive fairness potential of FSD, present in the literature: early bias correction, fair societal representation, and normative artefact construction. The first and the third are conceptually fragile. The second has potential, but only if the techniques it fits are reconceptualised through possible worlds semantics and the selection of protected attributes is grounded in a defensible social ontology. Benchmarking analysis reveals that FSD has no significant statistical fairness advantages over traditional methods while being less trustworthy. At the same time, FSD can reduce implementation costs and limit the exposure of predictors to normative scrutiny. This creates a paradox: techniques proposed as fairness solutions increase organisational autonomy from the obligations that the field of algorithmic fairness puts forward. FSD thus follows broader techno-solutionist trends where innovation helps organisations avoid reciprocal relationships with society. The paper concludes with broad recommendations for the responsible deployment of FSD.