Better than the Real Thing? Promises and Perils of Synthetic Data: An Overview of Professor Peter Lee’s Essay Published in VERDICT

EXECUTIVE SUMMARY:

Professor Peter Lee’s VERDICT essay argues that synthetic data may revolutionize AI development by providing scalable, legally safer training material. Yet he warns that artificial datasets introduce new risks such as model collapse, bias, and misuse that demand proactive legal oversight. Rather than replacing existing regulatory debates, synthetic data transforms them, requiring courts, policymakers, and information professionals to rethink how innovation, privacy, and intellectual property intersect in the AI era

BETTER THAN THE REAL THING?

Professor Peter Lee’s February 2026 VERDICT column examines one of the most consequential developments in artificial intelligence, the growing reliance on synthetic data, artificially generated datasets used to train AI systems instead of real-world information. He frames synthetic data as both a technological breakthrough and a source of new legal and ethical risk.

Core Thesis

Lee argues that synthetic data may fundamentally reshape AI development by addressing major legal obstacles including privacy violations, copyright exposure, and bias in training datasets, but it does not eliminate governance challenges. Instead, it shifts risks into new forms that regulators and courts must confront.

According to the VERDICT summary, synthetic data offers “unlimited, high-quality training content,” yet it raises concerns such as model collapse, new biases, and the potential for harmful applications if deployed irresponsibly.

Key Themes and Arguments

1. Why Synthetic Data Matters

Lee explains that traditional AI training depends heavily on vast quantities of real-world information. That dependence has created persistent legal problems:

• Privacy risks when models ingest sensitive data

• Copyright liability from scraping copyrighted works

• Structural bias reflecting real-world inequalities

Synthetic datasets promise to reduce these issues because they can be generated artificially rather than collected from individuals or copyrighted sources.

Legal significance:
For information professionals and IP scholars, Lee’s analysis situates synthetic data as a potential workaround to existing regulatory frameworks that were built around human-generated content.

. Advantages: Efficiency, Compliance, and Innovation

Lee highlights several benefits:

• Reduced legal exposure.  Artificial datasets may avoid many copyright and privacy claims.

• Scalability.  Synthetic content can be produced in massive quantities.

• Technical flexibility.  Researchers can simulate rare or dangerous scenarios (e.g., autonomous driving or medical testing).

From an innovation law perspective, consistent with Lee’s broader scholarship on technology and patents, the use of synthetic inputs could accelerate AI research while sidestepping bottlenecks created by data ownership disputes.

3. Risks: Model Collapse, Bias, and Governance Gaps

Lee cautions that synthetic data is not a cure all.

He warns about:

• Model collapse: AI systems trained repeatedly on AI generated content may degrade in quality over time.

• New forms of bias: Artificial datasets can encode design assumptions that reproduce or even amplify inequities.

• Dual use concerns: Synthetic data could facilitate dangerous AI applications, from deepfakes to automated misinformation.

These risks underscore Lee’s broader theme: technological solutions often create second order regulatory problems rather than eliminating them.

4. The Need for Thoughtful Regulation

Rather than endorsing blanket restrictions or uncritical adoption, Lee advocates a balanced regulatory framework emphasizing responsible deployment.

His analysis suggests:

• Policymakers should treat synthetic data as a distinct category of AI governance.

• Legal doctrine must adapt to technologies that blur the line between real and artificial information sources.

• Courts and regulators should anticipate unintended consequences rather than reacting after harms emerge.

Broader Intellectual Context

Lee’s essay in VERDICT fits squarely within his larger body of work on innovation law and AI policy. His scholarship often explores how intellectual property systems and emerging technologies interact with social and legal institutions, particularly where new technologies challenge existing doctrinal assumptions.

Why This Article Is Considered “Seminal”

The piece is notable because it:

• Moves the debate beyond copyright lawsuits and AI training disputes to examine the future structure of data ecosystems.

• Bridges technical AI development with legal theory, an approach that resonates strongly with law librarian and legal information audiences.

• Offers an early framework for understanding synthetic data as both a compliance tool and a governance challenge.

Why Law Librarians and Legal Information Professionals Should Care

Professor Peter Lee’s discussion of synthetic data is not merely a technical AI debate, it signals a structural shift in how legal information is created, curated, and evaluated. For law librarians and other legal information professionals, synthetic datasets may soon shape the research tools used by courts, law firms, and scholars. As vendors increasingly rely on AI generated training materials to reduce copyright and privacy exposure, information professionals will need to understand how these systems are built, what sources they rely on, and where hidden biases or gaps may emerge. Evaluating the provenance, reliability, and transparency of AI-driven research platforms will become as important as traditional source evaluation once was for print reporters and citators.

Equally important, synthetic data raises new questions about authority and authenticity, core concerns for legal research specialists. If future legal analytics tools rely partly on artificial datasets, librarians may need to guide users in distinguishing between primary law, secondary analysis, and AI-generated approximations of legal patterns. Issues such as model collapse, data drift, and algorithmic bias directly affect the integrity of legal research outcomes. As trusted intermediaries between technology vendors and legal researchers, law librarians are uniquely positioned to advocate for ethical data practices, demand transparency from AI providers, and educate attorneys and judges about the strengths and limits of synthetic data driven legal tools.

MORE ABOUT SYNTHETIC DATA:

Synthetic Data and the Future of AI by Peter Lee