Good quality AI-generated synthetic data can reduce bias in datasets
by representing data with appropriate balance, density, distribution, and other crucial parameters. Synthetic data also provides the foundations for explainable AI or XAI. Algorithmic audits
need synthetic data, that is free to share with regulators and provides a window into the workings of AI algorithms. Where sensitive training data cannot be shared further, highly representative synthetic data can serve as a drop-in placement to provide model documentation, model validation, and model certification
. Synthetic data generated by MOSTLY AI's synthetic data platform corrected a skew towards racial bias in crime prediction from 24% to just 1 % and narrowed the gap between high-earning men and women from 20% to 2% in the US census dataset.
Read the Fairness Series to learn how to generate fair synthetic data!