Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting
This work addresses the comprehensibility and maintainability of legal drafting for legal professionals and computational law researchers, representing an incremental application of software engineering concepts to a new domain.
The paper tackles the problem of identifying problematic patterns in legal texts, termed 'law smells', by developing a taxonomy and detection methods, and demonstrates their utility on the United States Code.
Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples - namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession -, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in practice using the United States Code as a test case. Our work demonstrates how ideas from software engineering can be leveraged to assess and improve the quality of legal code, thus drawing attention to an understudied area in the intersection of law and computer science and highlighting the potential of computational legal drafting.