When Legal Terminology is Correct But the Answer is Still Wrong

2026-05-20 · Source: Artificial Lawyer · Field: Legal & Regulatory — Legal Technology (LegalTech), Compliance & Risk Management · Depth: Intermediate, short

Summary

Michael Krallmann, CEO of TransLegal, highlights a significant risk in legal AI: its ability to generate outputs that are terminologically correct but legally inaccurate, particularly in cross-border applications. While legal AI can produce definitions and translations that appear sound, it often fails to capture the underlying legal meaning, purpose, scope, and consequences of concepts across different jurisdictions. For instance, "liquidated damages" in common law and "contractual penalty clauses" in civil law systems are often mapped terminologically but diverge materially in enforceability. This can lead lawyers to apply incorrect legal frameworks, as familiar terms automatically trigger embedded assumptions. The core issue lies in the training data of foundation models, which lack structured representations of how legal concepts relate across jurisdictions. TransLegal addresses this by building human-curated legal datasets that explicitly map concepts and identify distinctions, using AI with expert review to surface differences rather than smooth them over.

Key takeaway

For legal professionals and AI product managers developing cross-border legal solutions, you must recognize that terminologically accurate AI outputs can still lead to incorrect legal conclusions. Your systems need explicit, structured data mapping legal concepts across jurisdictions, rather than relying solely on large language models. Prioritize solutions that surface conceptual differences, enabling you to avoid activating incorrect legal frameworks and mitigate significant risks in international legal practice.

Key insights

Legal AI outputs can be terminologically correct yet legally wrong, especially in cross-border contexts, due to data limitations.

Principles

• Correct terminology does not guarantee correct legal meaning.
• Legal concepts are defined by purpose, scope, and consequences.
• Data must explicitly represent cross-jurisdictional legal distinctions.

Method

TransLegal builds structured, human-curated legal datasets mapping concepts across jurisdictions, using AI with expert review to validate distinctions.

In practice

• Scrutinize legal AI outputs for underlying legal meaning, not just terminology.
• Identify where legal concepts diverge across jurisdictions.
• Implement expert-in-the-loop validation for AI-generated legal data.

Topics

• Legal AI
• Cross-border Law
• Legal Terminology
• Data Curation
• Jurisdictional Differences
• TransLegal

Best for: Product Manager, CTO, VP of Engineering/Data, Domain Expert, AI Product Manager, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Lawyer.