Can synthetic data ever be truly ethical?

Synthetic data offers a promising avenue for AI development, helping teams avoid some ethical risks associated with real-world data. However, the ethics of synthetic data are not guaranteed by its artificial origin alone. Ethical outcomes depend heavily on how synthetic data is generated, validated, and applied.

Understanding Synthetic Data

Synthetic data is artificially generated rather than collected from real-world events or individuals. It is designed to mimic the statistical properties of real datasets, often with the goal of enhancing privacy and reducing reliance on sensitive personal data. While this approach can reduce certain risks, synthetic data is not inherently bias-free or ethically neutral. If poorly designed, it can replicate or even amplify existing ethical issues.

Critical Ethical Considerations for Synthetic Data Usage

• Representation and Bias: Synthetic data generators often learn from real datasets. If those source datasets contain demographic imbalances or systemic bias, the synthetic outputs will reflect the same issues. For example, a synthetic dataset that overrepresents one demographic group may lead to AI models that perform poorly for others. Ensuring demographic diversity in synthetic datasets is essential to prevent reinforcing existing inequalities.
• Transparency and Documentation: One of the most common ethical gaps in synthetic data projects is insufficient documentation. Without clear metadata, it becomes difficult to audit how the data was created, what assumptions were embedded, or where bias might exist. Ethical synthetic data practices require transparent documentation of generation methods, source data characteristics, algorithms used, and validation steps. This aligns closely with the principles outlined in our AI Ethics and Responsible AI policy.
• Intended Use and Application: Ethical risk varies significantly depending on how synthetic data is used. Using synthetic data for internal testing or stress simulations carries different implications than deploying it in high-impact domains such as the healthcare industry. In sensitive contexts, synthetic data must be rigorously validated against real-world conditions to avoid harmful or misleading outcomes. Ethical oversight is essential to ensure suitability for purpose.

Avoiding Common Ethical Pitfalls in Synthetic Data

• Misunderstanding Limitations: Synthetic data is not a full replacement for real-world data. While it can fill gaps and support experimentation, it cannot eliminate the need for real-world validation, especially in high-stakes applications. Over-reliance on synthetic data without grounding in reality can result in brittle or unsafe models. This reinforces the continued importance of responsible AI data collection.
• Contributor Representation: Even though synthetic data does not directly involve individuals, ethical questions still arise when demographic traits or cultural characteristics are modeled. If marginalized communities are represented in synthetic datasets without consideration of context, fairness, or impact, ethical concerns persist. Representation without responsibility can still cause harm.
• Balancing Quality and Quantity: The ease of generating large volumes of synthetic data can encourage a “more is better” mindset. However, excessive data volume does not guarantee better outcomes. Ethical data practices prioritize high-quality, well-validated, and representative datasets. Quantity should never come at the expense of realism, fairness, or interpretability.

Practical Takeaways for Ethical Synthetic Data Practices

1. Fair and Responsible Generation Techniques: Use generation methods that explicitly account for fairness and diversity. Validate synthetic outputs against real-world benchmarks to ensure balanced representation.
2. Comprehensive Documentation: Maintain detailed metadata describing generation processes, assumptions, source data influences, and known limitations. This supports transparency and auditability.
3. Ethical Oversight: Establish governance structures that include ethical review of synthetic data projects. Regular audits can help detect and address bias before deployment.
4. Engagement with Communities: When synthetic data models demographic or cultural traits, engage with relevant communities where possible. Respectful engagement helps align technical goals with social responsibility.

Conclusion

Synthetic data can play a powerful role in building more privacy-aware and scalable AI systems, but it is not ethically “safe by default.” Its ethical value depends on intentional design, rigorous validation, transparent documentation, and strong governance. By embedding ethical thinking into every stage of synthetic data development, AI teams can harness its benefits while contributing to a more responsible and equitable AI ecosystem.