Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2023

Abstract

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 textbased 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.

Keywords

Law, Natural language processing, Network analysis, Refactoring, Software engineering

Discipline

Artificial Intelligence and Robotics | Law | Science and Technology Law

Research Areas

Innovation, Technology and the Law

Publication

Artificial Intelligence and Law

Volume

31

Issue

2

First Page

335

Last Page

368

ISSN

0924-8463

Identifier

10.1007/s10506-022-09315-w

Publisher

Springer

Additional URL

https://doi.org/10.1007/s10506-022-09315-w

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