Synthetic Data Generation

Synthetic data generation is defined under california-ab-2013-generative-ai-training-data-transparency as "a process in which seed data are used to create artificial data that have some of the statistical characteristics of the seed data."

This technique is commonly used in generative-ai development to augment training datasets, address data scarcity, protect privacy, or improve model performance on specific tasks. California's transparency law requires developers to disclose whether their systems use synthetic data generation and may include descriptions of the functional need or desired purpose of the synthetic data.

Synthetic data generation represents a key technique in modern AI development that balances the need for large training datasets with privacy, copyright, and data availability constraints. The California law's disclosure requirements reflect growing regulatory attention to the composition and sources of AI training data.