Comparative law and artificial intelligence might initially seem to occupy very different fields. One is concerned with understanding relationships between legal systems, while the other is concerned with building systems capable of generating, analysing and reasoning with information. Cross-border legal AI increasingly brings the two together.

A system operating within a single jurisdiction can work largely within the concepts, sources and assumptions of that legal system. An international system has a harder problem because it also needs to understand how concepts from different jurisdictions relate.

Sometimes that relationship is relatively straightforward. In other cases, two concepts perform similar functions but differ in scope, or legal systems solve the same practical problem through entirely different doctrinal structures. There will also be situations in which no useful direct equivalent exists.

Comparative lawyers have developed methods for analysing precisely these questions. They look beyond terminology to examine function, doctrine, institutional setting, legal effect and context, and are accustomed to conclusions that fall somewhere between concepts being simply the same or different.

This methodology is increasingly relevant to AI. A cross-border application needs some mechanism for determining whether information from one jurisdiction can safely be applied to another. Where the connection is based predominantly on linguistic similarity, there is a risk that familiar labels become a proxy for legal equivalence.

Comparative analysis allows relationships to be represented in greater detail. Concepts can be linked according to degrees of equivalence, similarities can coexist with recorded differences and a concept can have several possible counterparts rather than being forced into a one-to-one relationship.

In this sense, comparative law can provide a method for structuring legal knowledge. At TransLegal, we are working on converting jurisdiction-specific and comparative analysis into structured information that downstream AI systems can retrieve and use. This reduces the extent to which the model has to infer comparative relationships from scratch whenever it encounters them.

For much of its history, comparative legal analysis has been time-consuming, expensive and difficult to perform at scale. AI creates the possibility of reusing comparative information across very large numbers of legal interactions, while at the same time making the quality of that information increasingly important.

As legal AI becomes more international, comparative law may therefore have a considerably larger role to play in legal technology than might initially appear, both as an area for AI-assisted research and as part of the underlying infrastructure needed for reliable cross-border applications.