A Stanford-trained scholar who holds both a law degree and a doctorate in computer science is joining Columbia Law School to reshape how the legal profession understands and uses artificial intelligence. Neel Guha, who began his position as an associate professor on July 1, brings a rare combination of technical depth and legal sophistication to one of the profession's most pressing challenges: how courts, regulators, and lawyers should think about AI systems that are already reshaping the legal landscape.

Why Does Law Need AI Experts Who Understand Both Disciplines?

Guha's path to this intersection reveals why the legal profession is scrambling to catch up with AI. While pursuing his undergraduate degree in computer science at Stanford University and a master's degree in machine learning at Carnegie Mellon University, he took a civil rights law course in Washington, D.C., that sparked a realization. "One of the things that shocked me was the extent to which law and computer science share deep similarities," he explained. "Both fields are fundamentally about designing systems under constraints. In law, you're designing rules and institutions; in computer science, you're designing algorithms and software".

That insight crystallized during a lecture on algorithmic fairness, the study of how to define, measure, and mitigate unfairness in automated decision-making systems. When a professor displayed a machine learning equation alongside the legal concept of "disparate impact," Guha recognized that his two passions could merge into something powerful. He returned to Stanford to pursue a joint J.D./Ph.D. in computer science, which he will complete in September.

What Are the Key Areas Where AI Is Transforming Law?

Guha's scholarship focuses on three interconnected domains where AI is already forcing the legal system to adapt:

• Public Policy and Regulation: How regulators should design rules that balance AI's risks against its social benefits, particularly in high-stakes domains like healthcare where an AI diagnostic tool could democratize access to care but also harm patients through misdiagnosis.
• Professional Practice: How lawyers can effectively evaluate and deploy AI tools within their own work, similar to how pharmaceutical companies conduct clinical trials or how banks conduct stress tests to ensure safety and reliability.
• Empirical Legal Research: Using machine learning to analyze entire bodies of law rather than representative samples, allowing scholars to measure what has happened across every statute at the state or federal level or every published court decision.

The third area represents a fundamental shift in how legal scholarship can be conducted. "We can ask what has happened across every single statute in this country at the state or federal level or every single published decision," Guha noted. "AI and machine learning can give us new ways to see the law in action and see how it is unfolding".

How Should Law Schools Prepare Students for an AI-Driven Legal System?

Guha will begin his tenure at Columbia Law teaching Torts, the foundational course that examines personal responsibility for one's actions. But he sees tort law as far more than a traditional subject. "Tort law is the cutting edge of modern AI governance," he explained. "Whether it's chatbot litigation, whether it's in the health care context or about self-driving cars, these cases are going to stretch the doctrine in new and interesting ways, as all or most technologies do".

His broader mission at Columbia involves building what he calls a shared institutional philosophy about AI. "There's an opportunity to build our philosophy about AI as a Columbia community," he stated, "and to develop innovative techniques to train law students who can effectively negotiate and traverse a professional terrain with this technology".

This educational challenge is urgent. As AI systems increasingly make or influence legal decisions, courts must grapple with questions that existing doctrine was never designed to answer. How should judges assign liability when an AI health tool causes harm? What standards should apply to AI systems used in criminal justice? How can lawyers ensure that AI-powered legal research tools are accurate and trustworthy? These questions require lawyers who understand not just the law, but the technical realities of how machine learning systems work.

Guha's appointment signals a broader recognition within legal academia that AI literacy is no longer optional for the profession. His unique background, combining deep technical expertise with sophisticated legal analysis, positions him to help shape how the next generation of lawyers will practice in an AI-augmented world.