Synthetic Data

Synthetic data is artificially created data designed to replicate the statistical properties, patterns, and structures of real-world data without containing any actual real-world observations. It is generated using techniques ranging from simple rule-based simulation to sophisticated generative AI models. A synthetic medical dataset might contain realistic patient records — with plausible demographics, diagnoses, and treatment outcomes — that correspond to no actual patient. Synthetic images can depict realistic faces, vehicles, or scenes that never existed in reality.

Synthetic data addresses several critical challenges in AI development. Privacy: real patient, financial, or personal data is heavily regulated — synthetic data enables model development without privacy risks. Scarcity: rare events (fraud, manufacturing defects, rare diseases) produce limited real training examples — synthetic data can generate unlimited instances. Imbalance: when one class vastly outnumbers another, synthetic examples of the minority class can balance the dataset. Cost: collecting and labeling real data is expensive — synthetic data can be generated at scale for a fraction of the cost. Bias: synthetic data can be intentionally designed to be more representative and balanced than biased real-world data.

Generating high-quality synthetic data is itself an AI challenge. GANs, VAEs, diffusion models, and large language models are all used to generate synthetic examples. The key quality criterion is that models trained on synthetic data should perform comparably to models trained on real data — this is validated through benchmarking on real test sets. Risks include distributing synthetic data that does not capture important real-world correlations (leading to models that fail in deployment), and the possibility that adversaries could reverse-engineer synthetic data to infer information about the real data it was based on.