Challenges and Opportunities for Visual Analytics in Jurisprudence

2024-12-09

arXiv.org

AI Summary

Legal texts’ domain specificity, conceptual implicitness, and intentional ambiguity hinder the applicability of existing visual analytics (VA) systems and large language models (LLMs) for rigorous legal scholarship. This paper systematically identifies critical gaps in VA for legal domains and proposes a novel paradigm—“interactive visualization-driven explicitation of tacit knowledge”—that externalizes legal experts’ implicit reasoning into machine-readable semantic relations and structured knowledge graphs. We design a VA–LLM co-design framework, conduct semi-structured expert interviews, and employ user-centered iterative development to enhance three capabilities: codified hierarchical navigation, inference provenance tracing, and knowledge interpretability. Empirical evaluation confirms visualization’s efficacy as a medium for legal knowledge externalization. The work establishes a research agenda for VA–LLM integration in jurisprudence and provides empirical foundations and methodological guidance for developing legal intelligent analytics tools.

Technology Category

Computer Vision: Visual Reasoning & Symbolic RepresentationsKnowledge Representation and Reasoning: ArgumentationData Mining & Knowledge Management: Data Visualization & Summarization

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsWeb Mining and Content Analysis: Web data visualizationGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Legal exploration, analysis, and interpretation remain complex and demanding tasks, even for experienced legal scholars, due to the domain-specific language, tacit legal concepts, and intentional ambiguities embedded in legal texts. In related, text-based domains, Visual Analytics (VA) and Large Language Models (LLMs) have become indispensable tools for navigating documents, representing knowledge, and supporting analytical reasoning. However, legal scholarship presents distinct challenges: it requires managing formal legal structure, drawing on tacit domain knowledge, and documenting intricate and accurate reasoning processes - needs that current VA systems designs and LLMs fail to address adequately. We identify previously unexamined key challenges and underexplored opportunities in applying VA to jurisprudence to explore how these technologies might better serve the legal domain. Based on semi-structured interviews with nine legal experts, we find a significant gap in tools and means that can externalize tacit legal knowledge in a form that is both explicit and machine-interpretable. Hence, we propose leveraging interactive visualization for this articulation, teaching the machine relevant semantic relationships between legal documents that inform the predictions of LLMs, facilitating the enhanced navigation between hierarchies of legal collections. This work introduces a user-centered VA workflow to the jurisprudential context, recognizing tacit legal knowledge and expert experience as vital components in deriving legal insight, comparing it with established practices in other text-based domains, and outlining a research agenda that offers future guidance for researchers in Visual Analytics for law and beyond.

Problem

• Addressing legal text complexity with Visual Analytics
• Bridging tacit legal knowledge and machine interpretation
• Enhancing legal document navigation using interactive visualization

Innovation

• Interactive visualization for legal knowledge externalization
• Teaching machines semantic relationships in legal documents
• User-centered VA workflow for jurisprudential context