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AI-generated synthetic data, in addition to protecting the privacy oforiginal data sets, allows users and data consumers to tailor data to theirneeds. This paper explores the creation of synthetic data that embodiesFairness by Design, focusing on the statistical parity fairness definition. Byequalizing the learned target probability distributions of the synthetic datagenerator across sensitive attributes, a downstream model trained on suchsynthetic data provides fair predictions across all thresholds, that is, strongfair predictions even when inferring from biased, original data. This fairnessadjustment can be either directly integrated into the sampling process of asynthetic generator or added as a post-processing step. The flexibility allowsdata consumers to create fair synthetic data and fine-tune the trade-offbetween accuracy and fairness without any previous assumptions on the data orre-training the synthetic data generator.

Strong statistical parity through fair synthetic data