Introduction: A New Hope — or Another Illusion?

As AI systems increasingly influence hiring, healthcare, finance, and justice, the data they’re trained on has come under intense scrutiny. The promise of synthetic data — artificially generated data that mimics real-world distributions — is rising as a way to bypass the messiness of human bias and the constraints of privacy law.

But are we solving bias or simply masking it behind a more sophisticated veil?What Is Synthetic Data — and Why Is It Booming?

Synthetic data is algorithmically generated rather than collected from real individuals. It’s designed to statistically resemble real data while reducing exposure to personally identifiable information (PII). Proponents claim this allows developers to:

• Reduce reliance on sensitive datasets

• Improve model generalizability

• Address underrepresented groups in imbalanced datasets

MarketsandMarkets estimates the synthetic data market will grow from $0.2B in 2022 to $1.2B by 2027, reflecting rising demand in regulated industries like healthcare and finance

The Ethical Pitch: A Fix for Bias and Privacy

Fairer Representation — Synthetic data can be generated to ensure minority or marginalized populations are better represented, thus mitigating the historical bias embedded in real-world datasets.2. Enhanced Privacy — Since it doesn’t rely on real individuals, synthetic data offers strong privacy guarantees — appealing to institutions governed by GDPR, HIPAA, or other data protection laws.3. Speed & Scale — It allows teams to generate massive datasets quickly without requiring data collection consent or access agreements, accelerating model development.However, bias is systemic — not just statistical.

The Problem: Garbage In, Garbage Synthesized

Many synthetic datasets are still generated from real data distributions. If the underlying data reflects bias — say, against Black loan applicants or women in leadership roles — then the synthetic data will often replicate those patterns.

“When synthetic data is trained on biased real-world data, it often reproduces — or even amplifies — those same biases.” — MIT Technology Review (2023)

This challenges the idea that synthetic data is bias-free. Unless teams interrogate the original datasets, they risk reinforcing the very disparities they seek to fix.

A False Sense of Fairness?

Synthetic data can offer the appearance of ethical AI without the substance — a phenomenon sometimes called fairwashing. It becomes a compliance tool rather than a justice mechanism.

A Nature Machine Intelligence paper warns that synthetic data can “conceal rather than correct” bias if used uncritically. It lulls teams into thinking they’ve “solved” fairness when they’ve simply outsourced it to a generative model trained on flawed assumptions.

Real-World Examples and Cautionary Tales

• Healthcare AI Models: In synthetic health record datasets, researchers found that rare disease representation remained poor — echoing real-world underdiagnosis and raising questions about equity in AI-driven diagnostics.

• Facial Recognition: Synthetic faces created to diversify training datasets have been found to disproportionately “smooth over” ethnic features, inadvertently whitening and homogenizing representations.

• Financial Modeling: Banks using synthetic data to comply with privacy regulations sometimes fail to audit whether their models still encode discriminatory lending practices.

The Path Forward: Ethical Guardrails for Synthetic Data

• Audit the Source — Synthetic data isn’t neutral if its foundation is flawed. Teams must audit the real data used to generate it, especially for historical and social bias.

• Validate Privacy and Utility — Use domain-relevant utility tests alongside privacy checks like membership inference and linkage risk.

• Governance & Transparency — Document who generated the data, how it was created, and what bias mitigation steps were applied.

• Human-in-the-Loop — Include ethicists, sociologists, and affected communities in review.

• Limit Synthetic-on-Synthetic Training — Mix with high-quality human data to avoid compounding artifacts.

Conclusion: Synthetic ≠ Safe

Synthetic data offers powerful tools for privacy and scale — but it is not a shortcut to fairness. At worst, it can mask deeper ethical rot. At best, it can be a component of a deliberate, transparent, and justice-informed AI pipeline.

In the quest for ethical AI, synthetic data is neither savior nor scapegoat. It’s a scalpel — and how we wield it will determine whether it heals or harms.

References

• MarketsandMarkets. (2022). Synthetic Data Market – Global Forecast. [Link] (https://www.marketsandmarkets.com/Market-Reports/synthetic-data-market-264135623.html)

• Privacy International. (2021). Can synthetic data protect privacy? [Link] (https://privacyinternational.org)

• MIT Technology Review. (2023). Synthetic data can’t fix AI’s bias problems. [Link] (https://www.technologyreview.com/2023/04/19/1071386/synthetic-data-cant-fix-ais-bias-problem)

• Veale, M., & Binns, R. (2022). Fairwashing and the Ethics of Generative Data. Nature Machine Intelligence, 4(9), 742–744. [Link] (https://www.nature.com/articles/s42256-022-00590-z)

• Chen, I.Y. et al. (2020). Ethical Machine Learning in Health Care. Annual Review of Biomedical Data Science, 3(1), 123–144. [Link] (https://www.annualreviews.org/journal/biodatasci)

• Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. FAT Conference. [Link] (http://gendershades.org/)

• NYU AI Now Institute. (2021). Algorithmic Impact Assessments: A Practical Framework. [Link] (https://ainowinstitute.org/reports.html)