AF-XRAY: Visual Explanation and Resolution of Ambiguity in Legal Argumentation Frameworks

2025-07-14

AI Summary

Non-experts struggle to identify sources of ambiguity in Argumentation Frameworks (AFs) and assess argument acceptability in legal reasoning. Method: We propose an explanation-oriented visualization approach integrating game-theoretic perspective-based hierarchical visualization of argument length, semantic role classification of attack edges, multi-solution overlay rendering, and automated identification of critical attack sets—grounded in abstract AFs, three-valued/bivalent semantics, and gamified derivation structures. Contribution/Results: This work is the first to embed semantic role labeling and critical attack set generation algorithms directly into the visualization pipeline, enabling precise localization of ambiguity origins and exploration of disambiguation pathways. Evaluated on real-world legal cases, our method systematically generates disambiguated interpretations and explicitly reveals how alternative assumptions affect conclusions, significantly enhancing non-experts’ comprehension of teleological legal reasoning.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Web data visualization

Abstract

Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded solutions, enabling users to pinpoint specific causes of ambiguity and explore alternative resolutions. We use real-world legal cases (e.g., Wild Animals as modeled by Bench-Capon) to show that our tool supports teleological legal reasoning by revealing how different assumptions lead to different justified conclusions.

Problem

• Identifying ambiguity sources in legal argumentation frameworks
• Explaining argument acceptance for non-experts
• Transforming ambiguous legal scenarios into grounded solutions

Innovation

• Layered visualizations for argument derivation structures
• Classification of attack edges by semantic roles
• Overlay visualizations for ambiguous semantics solutions