Unveiling legal complexity

Abstract

Navigating and interpreting the vast landscape of legal corpora demands a thorough understanding of individual documents and the broader context in which they are situated. In support of this, visual analytics tools have been proposed to reveal the underlying relationships and contextualize legal data. This systematic literature review explores such tools, elucidating the foundational works, current practices, and the trajectory of this promising field. It also highlights significant legal visualization techniques, categorizing methods, identifying prominent authors and journals, and extrapolating trends and areas that need exploration. Ultimately, the review provides a missing taxonomy to law experts and offers a framework to technologists when developing visualization solutions for the legal field. Most of the work within the domain leverages network graphs, with increased integration of machine learning and natural language processing techniques for enhanced representation. This study also reveals a surge in interest post-2014, correlating with advancements in data-driven tools. Despite advancements, gaps exist, notably the absence of empirical validations with end-users and lack of interdisciplinary collaboration. This review aims to steer future research to address these voids and enhance the utility and comprehensibility of legal visualizations.