Synthetic data is moving from niche technique to default pipeline input—but the governance bar is rising just as fast. Two pieces today frame the trade: speed and privacy benefits versus integrity, bias, and performance risks when synthetic datasets aren’t tightly controlled.
3 Questions: The pros and cons of synthetic data in AI
MIT News published a Q&A with MIT researcher Kalyan Veeramachaneni on where synthetic data helps—and where it can quietly hurt—AI development. He points to practical upside: synthetic data can preserve privacy by mimicking real records without exposing personal information, reduce data acquisition/labeling costs, and speed up model development cycles when real data is scarce or sensitive.
The interview also flags the core technical risk: if synthetic data isn’t generated and validated carefully, model performance can degrade (for example, by missing edge cases or distorting important distributions). MIT cites an estimate that more than 60% of AI data in 2024 was synthetic, with continued growth expected across industries—raising the stakes for teams to treat synthetic generation as an engineering discipline, not a shortcut.
- Privacy-by-design is becoming operational: synthetic data can reduce exposure of personal data, but only if teams can demonstrate how generation prevents leakage and re-identification risk in practice.
- Performance risk shifts left: the biggest failure mode is silent—models that look good on synthetic-heavy validation but underperform in production because the synthetic distribution diverges from reality.
- Governance needs metrics, not slogans: adoption at “60%+” scale implies auditors and internal reviewers will expect documented utility tests, drift checks, and traceability from source data to synthetic outputs.
Synthetic data created by generative AI poses ethical challenges
NIEHS published an opinion piece arguing that generative AI-created synthetic data introduces ethical risks that can undermine scientific integrity and societal trust. The concern isn’t just privacy; it’s that synthetic datasets may encode biases, inaccuracies, or artifacts that look plausible enough to pass casual review—then propagate through downstream analyses and publications.
The piece calls for strategies to mitigate these issues, with an emphasis on preventing biased or incorrect synthetic data from being treated as a drop-in replacement for real-world evidence in research settings. For organizations using synthetic data to unlock access or accelerate studies, the message is clear: the ethical burden doesn’t disappear when the data is “synthetic”—it moves to how the data was generated, validated, and communicated.
- Integrity controls must match the use case: synthetic data used for exploratory work is a different risk profile than synthetic data used to support scientific claims or policy decisions.
- Bias can be amplified, not reduced: if the generator learns biased source distributions (or introduces new artifacts), synthetic datasets can entrench errors at scale.
- Disclosure becomes part of compliance: teams should be prepared to document synthetic provenance, validation steps, and limitations so stakeholders don’t over-trust generated datasets.