English

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

Artificial Intelligence 2025-07-16 v1

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.

Keywords

Cite

@article{arxiv.2507.10831,
  title  = {AF-XRAY: Visual Explanation and Resolution of Ambiguity in Legal Argumentation Frameworks},
  author = {Yilin Xia and Heng Zheng and Shawn Bowers and Bertram Ludäscher},
  journal= {arXiv preprint arXiv:2507.10831},
  year   = {2025}
}

Comments

International Conference on Artificial Intelligence and Law (ICAIL), June 16-20, 2025. Chicago, IL, USA