Multilingual legal AI needs better data, not better models, says TransLegal CEO

Legal AI systems often fail in cross-border contexts not because their underlying models lack capability, but because they lack the structured data needed to understand how legal concepts differ between jurisdictions. The industry's instinct to chase better models is misguided, as legal meaning varies by culture, history, and institutional practice. Scale alone cannot solve this problem, as many critical jurisdictional differences are subtle, under-documented, or embedded in practice. Proper comparative law analysis identifies purpose, scope, legal effect, and limitations, recognizing partial equivalence and non-equivalence. This kind of analysis does not emerge automatically from large document collections; it must be deliberately created by experts. A system designed for cross-border legal work needs structured representations of legal concepts mapped across jurisdictions, curated by specialists, and quality controls that reflect legal reasoning. Prompting and retrieval systems cannot fix the problem of conceptual misalignment. Systems that smooth away differences rather than making them visible expose users to hidden risk. Organizations deploying legal AI across markets should ask harder questions about data provenance and structure, considering transparency and accountability. The most valuable legal AI systems will be those that help users avoid mistakes by making jurisdictional differences visible rather than hiding them.

(Source：Complete Ai Training)