Lawmaps: enabling legal AI development through visualisation of the implicit structure of legislation and lawyerly process

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.

Understanding Self-Driving Vehicles - Current State and Challenge

“…According to this line of thinking and proposed a lane change assistance and automated motorway driving represent the next step, with Tesla's "autopilot" already making significant progress toward achieving this latter objective [8][9][10]. Authors such as and also conducted a research on cruise control and anti-skid braking systems, which are already widely used, represent the beginnings of autonomous driving [11][12][13].…”