Lawmaps: Enabling Legal AI development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process

TL;DR

This paper introduces Lawmaps, a visual modelling approach using UML-inspired diagrams to represent legal structures and processes, aiming to enhance legal knowledge accessibility and support legal AI development.

Contribution

It presents a novel visual modelling methodology called Lawmaps for representing legislation and legal processes, demonstrated through case studies in UK conveyancing and tenancy law.

Findings

Lawmaps effectively visualise legal structures and processes.

The methodology facilitates legal knowledge formalisation.

Preliminary results show potential for accelerating legal AI development.

Abstract

Modelling that exploits visual elements and information visualisation are important areas that have contributed immensely to understanding and the computerisation advancements in many domains and yet remain unexplored for the benefit of the law and legal practice. This paper investigates the challenge of modelling and expressing structures and processes in legislation and the law by using visual modelling and information visualisation (InfoVis) to assist accessibility of legal knowledge, practice and knowledge formalisation as a basis for legal AI. The paper uses a subset of the well-defined Unified Modelling Language (UML) to visually express the structure and process of the legislation and the law to create visual flow diagrams called lawmaps, which form the basis of further formalisation. A lawmap development methodology is presented and evaluated by creating a set of lawmaps for the practice of conveyancing and the Landlords and Tenants Act 1954 of the United Kingdom. This paper is the first of a new breed of preliminary solutions capable of application across all aspects, from legislation to practice; and capable of accelerating development of legal AI.