Rule-adhering synthetic data -- the lingua franca of learning
This work addresses the need for safe and representative synthetic data that can be shared as a common source of intelligence for humans and machines, though it appears incremental in its approach.
The paper tackles the problem of incorporating domain expertise into AI-generated synthetic data to ensure it adheres to statistical properties and pre-existing rules, demonstrating the concept on a publicly available dataset and evaluating benefits through descriptive analysis and downstream ML models.
AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within the original semantics. In this work we explore approaches of incorporating domain expertise into the data synthesis, to have the statistical properties as well as pre-existing domain knowledge of rules be represented. The resulting synthetic data generator, that can be probed for any number of new samples, can then serve as a common source of intelligence, as a lingua franca of learning, consumable by humans and machines alike. We demonstrate the concept for a publicly available data set, and evaluate its benefits via descriptive analysis as well as a downstream ML model.