For Heads-of · Practitioner
Synthetic Data
Artificially generated data created by algorithms or models, used as a substitute for or supplement to real data in training and testing.
- synthetic data
- data generation
- privacy
- model training
Benefits
Synthetic data can expand small datasets, balance class distributions, and preserve privacy by replacing sensitive real data without exposing individuals.
Risks
Synthetic data may not capture real-world complexity. If generated by a biased model, the synthetic data perpetuates those biases. Over-reliance on synthetic data can cause model collapse.
Governance Requirement
Organizations using synthetic data must disclose this, understand its limitations, and validate that models trained on synthetic data perform adequately on real data.