Is Synthetic Data a Reliable Option for Training Machine Learning Models? Is Synthetic Data a Reliable Option for Training Machine Learning Models?
Getting enough of the right data is one of the most persistent challenges in machine learning. Growing privacy concerns make this... Is Synthetic Data a Reliable Option for Training Machine Learning Models?

Getting enough of the right data is one of the most persistent challenges in machine learning. Growing privacy concerns make this even more of an obstacle. Amid these difficulties, synthetic data has emerged as a promising solution.

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What Is Synthetic Data?

Synthetic data is information that doesn’t come from real-world events or people. However, it’s not just numbers you pull out of thin air. It’s the product of generative AI models that learn how real-world data behaves and mimic these relationships and trends in their output.

There are three main generation methods for synthetic data — statistical distribution, model-based generation and deep learning. The first is the most straightforward. Here, data scientists or AI models analyze a real-world dataset’s statistical distribution to create a synthetic one with the same characteristics.

Model-based generation goes a step further. Instead of a simple algorithm, you use machine learning models to analyze the original data, picking up on subtler characteristics and relationships beyond statistical distributions.

As you’d assume from the name, deep learning methods use deep learning models to analyze and create datasets. These models — often generative adversarial networks (GANs) — repeatedly categorize and generate information to produce highly detailed datasets that can reflect complex relationships and trends.

Regardless of the specific method, all synthetic data performs the same function. It behaves almost identically to real-world data without reflecting actual people, places, things, or events.


Synthetic Data vs. Real Data for Machine Learning

The most obvious advantage of synthetic data is that it contains no personally identifiable information (PII). Consequently, it doesn’t pose the same cybersecurity risks as conventional data science projects. However, the big question for machine learning is whether this information is reliable enough to produce functioning ML models.

The answer is that it depends on the situation. One MIT study found that ML models trained on synthetic data perform even better than conventional versions in machine vision applications. This accuracy may stem from the fact that you can ensure synthetic data is consistent and well-formatted — something that isn’t always possible with real-world information.

Conditions are rarely perfect in the real world. Photos can be blurry, survey respondents may leave entries blank, and data formats may not be standardized. These inconsistencies make it difficult for AI models to use data to its full extent. By contrast, you can manipulate synthetic data to have none of these issues without changing the information itself.

In other situations, synthetic data isn’t quite so reliable. A different study on health information classification systems found that synthetic data-trained models were consistently less accurate, though not by a huge margin.

Real-world data has the advantage of directly representing the world. Synthetic data may be neater, but it can be too neat — it may omit statistical outliers. It’s also worth noting that you still need real information to base a synthetic dataset on, introducing another layer for errors and inaccuracies to occur.

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When to Use Synthetic Data in Machine Learning

Given these pros and cons, there’s no one-size-fits-all answer to whether synthetic data is the best choice for machine learning. It all comes down to the specific application and dataset in question.

Some of the most widely used AI applications today are service personalization or fraud detection. Synthetic data isn’t ideal in these use cases. You must tailor results to specific people for these applications, so information that doesn’t reflect real people won’t work.

Similarly, analyses where outliers matter more than larger trends are best for real-world data. That’s not an issue for many ML models, but you’ll want to consider outliers in some types of medical research or other people-focused analyses.

By contrast, synthetic data is often better than real-world data for more general analysis and classification tasks. Supply chain predictions, machine vision, and risk analysis all benefit from synthetic data.

Synthetic datasets are also the way to go when dealing with highly sensitive information. This is most relevant in tightly regulated industries like health care or government services. Noncompliance fines for regulations like HIPAA can be as high as $50,000 — more in some sectors or areas — so the extra privacy can be a lifesaver in these cases. 

Synthetic Data Is Imperfect but Helpful

Synthetic data isn’t perfect for every machine learning application, but neither is real-world data. In many cases, AI-generated datasets are just as if not more reliable than real-world information. Making the right choice starts with understanding each side’s benefits and strengths. Once you know why synthetic data may be more or less helpful, you can use it safely.

Zac Amos

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.