Synthetic data acts as a “hidden lever” in responsible AI by enabling organizations to train, test, and validate AI models without violating privacy, using copyrighted material, or relying on biased real-world datasets. It allows for the deliberate creation of diverse, balanced datasets, transforming AI development from reactive bias correction to proactive “fairness by design“.
In a recent analysis, Professor Peter Lee of UC Davis School of Law argues that synthetic data could reshape the legal and economic landscape of AI. For organizations navigating compliance, intellectual property risks, and data privacy obligations, this development deserves close attention. Synthetic datasets promise to reduce reliance on sensitive real-world information, potentially lowering exposure to copyright disputes and privacy liabilities. For executives responsible for innovative budgets and risk management, that sounds like a compelling proposition.
Yet the opportunity comes with tradeoffs. Synthetic data does not eliminate risk — it transforms it. Lee highlights issues such as hidden bias, model degradation, and governance challenges when artificial datasets begin influencing real-world decision making. In other words, the question for leadership is not whether to adopt AI tools, but how to ensure that the data behind them remains trustworthy and aligned with organizational values.