Synthetic Data: Training AI Without Real People's Information

Training a machine learning model requires data. This is not a subtle point. The model learns from examples, and the quality of what it learns is bounded by the quality of the examples it sees. For a lot of the domains where AI could do the most good, that creates an uncomfortable problem: the most useful training data is also the most sensitive.

Consider healthcare. A model that could help diagnose rare diseases would ideally train on patient records from thousands of cases. Those records contain diagnoses, lab values, imaging results, medication histories, and demographic data. They are also among the most legally protected categories of personal information in existence. In the United States, HIPAA creates significant barriers to using real patient records for model training outside of very controlled research settings. In Europe, GDPR adds additional constraints. Even with consent, aggregating patient data across institutions is operationally complex and legally uncertain.

Synthetic data is one response to this tension. The basic idea is to generate artificial data that has the same statistical properties as real data but does not correspond to any real individual. If you train a generative model on real patient records and use it to produce a million synthetic patient records, the synthetic records will have plausible distributions of age, diagnosis, lab values, and outcomes. A classifier trained on that synthetic data should, in theory, learn the same patterns it would have learned from real data. But no real person's information was in the training set.

This works reasonably well in practice for some purposes. The most common generation approaches are statistical models that sample from fitted distributions, generative adversarial networks (GANs), and more recently, large language models fine-tuned on structured data. Each has different tradeoffs. Statistical models are transparent and auditable but can miss complex correlations. GANs can capture richer patterns but are harder to train and harder to inspect. LLM-based approaches are newer and not yet as well understood in production settings.

The part that gets less attention in popular discussions is the fidelity-privacy tradeoff. To be useful for training, synthetic data has to be statistically faithful to the real data. It needs to capture the same distributions, the same correlations between variables, and importantly, the same rare events and edge cases that are often the most important training signal. A synthetic medical dataset that does not capture rare disease presentations is less useful for training diagnostic models. But the closer the synthetic data is to the real data statistically, the more information it carries about the original. Membership inference attacks, where an adversary uses the trained model to infer whether a specific individual was in the training set, become more feasible when the synthetic data is more faithful. Attribute inference attacks, where an adversary infers sensitive attributes of individuals from patterns in synthetic data, are a related concern.

There is no version of synthetic data that is perfectly private and perfectly faithful. The tradeoff is real. What organizations and researchers can do is characterize the tradeoff explicitly, measure the privacy risk under plausible threat models, and make deliberate choices about where on that spectrum to operate for a given use case.

The use cases that are real and widely discussed in the research literature include synthetic medical records for training diagnostic models, synthetic financial transaction data for fraud detection, and synthetic scenarios for training autonomous vehicles, particularly rare events like unusual road conditions or accident scenarios that would be dangerous or impossible to capture in sufficient volume from real driving. In each of these cases, the value of synthetic data is not that it perfectly replicates real data but that it provides a practical substitute for data that is either too sensitive, too rare, or too costly to collect at scale.

Gartner has identified synthetic data generation as an emerging capability for enterprise AI development, noting its role in addressing both data scarcity and privacy constraints. According to Gartner's newsroom, the technology is moving from research settings into enterprise workflows, particularly in regulated industries where access to real training data is most constrained.

The governance question that I find most interesting, and most unresolved, is the one about the relationship between synthetic data and the people whose data generated it. If an organization trains a generative model on its customer database and uses that model to produce synthetic data for building a downstream AI product, the customers' information shaped the synthetic distribution. Their patterns, their correlations, their edge cases are encoded in the generator. Is that data truly anonymized? Do those customers have any right to know? Legal frameworks on this are still developing. The EU's approach under GDPR is cautious: synthetic data is not automatically exempt from data protection rules just because it contains no directly identifying information. The question is whether the synthetic data is genuinely unlinkable to real individuals, which depends on the specific generation method and the specific threat model, not on a general claim that "it's synthetic."

From an IS ethics standpoint, this matters because organizations that adopt synthetic data as a privacy solution may be solving one problem while creating another. If the privacy guarantee is weaker than claimed, and if downstream AI products built on synthetic data behave in ways that affect real people, the causal chain from real customer data to real outcome still exists, even if there is no real record in the training set. The accountability question does not disappear because the data was labeled synthetic.

I do not think this means synthetic data is not valuable. It clearly is, and in domains where the alternative is no model at all due to data access constraints, synthetic data is a real improvement. The honest position is that it is a tool with specific capabilities, specific limitations, and a privacy profile that needs to be evaluated case by case rather than assumed to be solved by the act of generation.