  AI-generated synthetic data, in addition to protecting the privacy of
original data sets, allows users and data consumers to tailor data to their
needs. This paper explores the creation of synthetic data that embodies
Fairness by Design, focusing on the statistical parity fairness definition. By
equalizing the learned target probability distributions of the synthetic data
generator across sensitive attributes, a downstream model trained on such
synthetic data provides fair predictions across all thresholds, that is, strong
fair predictions even when inferring from biased, original data. This fairness
adjustment can be either directly integrated into the sampling process of a
synthetic generator or added as a post-processing step. The flexibility allows
data consumers to create fair synthetic data and fine-tune the trade-off
between accuracy and fairness without any previous assumptions on the data or
re-training the synthetic data generator.
