Training AI Without Compromise
With the rise of AI, the demand for training data is insatiable. However, using customer data to train models is fraught with ethical and legal peril. Enter Synthetic Data.
What is Synthetic Data?
It is statistically mirrored data generated by algorithms. It maintains the same distribution, correlation, and patterns as real data, but contains no actual user info. For example, if your real user base is 60% mobile users, your synthetic dataset will also be 60% mobile users, but 'Alice from New York' becomes 'Jane from Ohio'.
This allows data scientists to train models on 'production-like' data without needing security clearance for the actual production database.
Bias in, Bias out
One major risk is ensuring your synthetic generation process doesn't amplify existing biases. If your real data historically rejected loans for a certain demographic, your synthetic data will learn to do the same. Ethical AI requires 'Fairness Constraints'—mathematical rules during generation to ensure balanced representation.
Tools for Generation
We explore open-source tools like SDV (Synthetic Data Vault) and commercial APIs from Gretel.ai that can take a CSV of real data and output a CSV of synthetic data that passes the Kolmogorov-Smirnov test for statistical similarity.