Synthetic Data for AI Training Under EU Rules

Yes, synthetic data can be used to train AI under EU rules, provided the data is created and used in a way that complies with the General Data Protection Regulation (GDPR) and the forthcoming EU AI Act. The key is that synthetic data must not contain personal data, or if it does, it must be processed under a lawful basis and with appropriate safeguards. This article explains how synthetic data fits into the EU regulatory framework, what obligations remain, and how to use it safely.

Background: Why Synthetic Data Matters

Synthetic data is artificially generated information that mimics the statistical properties of real-world data without containing actual records of individuals. It is created by algorithms that learn patterns from a source dataset and then generate new, plausible data points. This is particularly valuable for training AI models when real data is scarce, sensitive, or subject to strict privacy rules.

Under the GDPR, any processing of personal data—including for AI training—requires a legal basis, such as consent or legitimate interest, and must respect principles like data minimization and purpose limitation. Synthetic data offers a way to reduce the risk of exposing personal information because it does not directly correspond to real individuals. However, the EU regulatory landscape is evolving, and the AI Act introduces new requirements for high-risk AI systems, including data governance and transparency.

How the GDPR Applies to Synthetic Data

The GDPR applies to personal data, which is any information relating to an identified or identifiable natural person. Synthetic data that is purely artificial and contains no link to a real person falls outside the GDPR’s scope. But the challenge is ensuring that the synthetic data cannot be re-identified. If the generation process retains enough details to allow reverse engineering, the data may still be considered personal.

In practice, regulators expect that synthetic data be generated with robust privacy guarantees, such as differential privacy, which adds controlled noise to prevent re-identification. If the synthetic dataset is truly anonymous, the GDPR does not apply. If it is only pseudonymized, it still counts as personal data and must comply with all GDPR obligations.

The EU AI Act: New Obligations for High-Risk AI

The EU AI Act, which entered into force in August 2024, imposes specific requirements on high-risk AI systems, including those used in critical infrastructure, education, employment, and law enforcement. For such systems, the AI Act mandates that training, validation, and testing datasets be relevant, representative, and free of errors. Synthetic data can be used to meet these requirements, but it must be documented and validated.

Article 10 of the AI Act emphasizes data governance, requiring that datasets be examined for biases and that appropriate measures be taken to mitigate them. Synthetic data can help address biases by generating balanced datasets, but the AI Act also requires transparency about the use of synthetic data. Providers of high-risk AI must include information about the data used in the technical documentation, and where synthetic data is used, it should be clearly indicated.

How to Choose the Right Synthetic Data Approach

Selecting the correct synthetic data method depends on your use case, the sensitivity of the source data, and the required level of fidelity. Here are the main approaches:

• Fully synthetic: No real data is used; the data is generated from scratch based on domain knowledge. This is safe but may lack realism.
• Partially synthetic: Some real records are replaced with synthetic ones, preserving statistical relationships while reducing risk.
• Generative models: Techniques like GANs or VAEs learn the distribution of real data and generate new samples. These can produce highly realistic data but require careful tuning.
• Differential privacy: Adding noise during generation provides a mathematical guarantee of privacy, but can reduce data utility.

For EU compliance, the most important factor is whether the synthetic data can be linked back to individuals. If there is any residual risk, treat it as personal data and apply GDPR safeguards.

Common Problems and Misconceptions

A common misconception is that synthetic data is automatically GDPR-compliant. This is not true. If the synthetic data is generated from personal data and retains identifiable patterns, it may still be considered personal data. Another issue is that synthetic data can perpetuate or even amplify biases present in the source data. If the original dataset is biased, the synthetic version will likely be too.

Another problem is the risk of overfitting: if the generative model memorizes specific records, it could leak sensitive information. Techniques like differential privacy mitigate this, but they can degrade data quality. Finally, there is the question of accountability. Under the AI Act, you must be able to explain how your AI was trained, including the provenance of the data. Simply saying “it’s synthetic� is not enough; you need to document the generation process and validate the output.

Costs and Practical Considerations

Implementing synthetic data generation involves costs, both in terms of computational resources and expertise. Generative models require significant processing power and skilled data scientists to train and evaluate. Open-source tools exist, but they still require integration and customization. On the other hand, synthetic data can reduce costs associated with data collection, cleaning, and privacy compliance, especially when dealing with large volumes of sensitive data.

For small and medium enterprises, the upfront investment might be a barrier. However, there are cloud-based services that offer synthetic data generation as a managed solution, which can lower the entry threshold. It is essential to weigh these costs against the potential penalties for non-compliance with GDPR or the AI Act, which can be substantial.

Safety and Ethical Considerations

Beyond legal compliance, there are ethical considerations. Synthetic data should not be used to mislead—for instance, by claiming it represents real user behavior when it does not. This could lead to AI systems that perform well in training but fail in real-world applications. Transparency is key: users and regulators should know when synthetic data has been used.

Additionally, synthetic data can be used to protect vulnerable groups by avoiding the collection of sensitive personal data. For example, in healthcare, synthetic patient records can enable research without exposing individual health information. But it is crucial to ensure that the synthetic data is not re-identifiable and that the generation process itself does not introduce new ethical risks, such as reinforcing stereotypes.

Alternatives to Synthetic Data

If synthetic data is not suitable, there are other ways to train AI under EU rules:

• Anonymization: Removing identifiers from real data, but this is often reversible and not always sufficient.
• Federated learning: Training models on decentralized data without moving the data to a central server, reducing privacy risks.
• Data minimization: Collecting only the minimum data necessary, which reduces the scope of GDPR obligations.
• Consent-based data collection: Obtaining explicit consent from individuals for the specific use of their data in AI training.

Each alternative has its own trade-offs in terms of privacy, cost, and data utility. The choice depends on your specific needs and regulatory constraints.

Frequently Asked Questions

Is synthetic data considered personal data under GDPR?

It depends. If the synthetic data is completely artificial and cannot be linked to any real person, it is not personal data. If it is generated from personal data and retains identifiable characteristics, it may still be personal data, so you must treat it as such.

Does the EU AI Act prohibit the use of synthetic data?

No, the AI Act does not prohibit synthetic data. In fact, it can be used to meet data governance requirements, provided it is relevant, representative, and documented. The key is to ensure the data is of high quality and that its use is transparent.

What is differential privacy and why is it important?

Differential privacy is a technique that adds controlled noise to data or queries, making it mathematically difficult to identify individuals. It is important because it provides a strong privacy guarantee, which helps in keeping synthetic data outside the scope of GDPR.

Can synthetic data introduce biases?

Yes, if the source data is biased, the synthetic data will inherit those biases. It is essential to evaluate the synthetic dataset for fairness and take corrective measures, as required by the AI Act.

Do I need to inform users if my AI was trained on synthetic data?

For high-risk AI systems, the AI Act requires transparency in technical documentation, which includes data provenance. For other systems, it depends on the context, but it is good practice to be transparent about the use of synthetic data.

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