An Argumentative Explanation Framework for Generalized Reason Model with Inconsistent Precedents

2025-10-22

AI Summary

To address the limitation of case-based reasoning (CBR) in legal AI—its reliance on the precedent consistency assumption—this paper proposes the Generalized Reason Model (GRM), the first explanatory framework enabling argumentative reasoning under inconsistent precedents. Methodologically, we extend the Derivable State Argument (DSA) framework by integrating nonmonotonic logic and a dynamic conflict-resolution mechanism, thereby supporting modeling, principled weighing, and traceable inference over conflicting precedents. Our key contributions are threefold: (1) we relax the traditional consistency constraint to establish the first explainable reasoning framework explicitly designed for inconsistent precedents; (2) we enable structured argument generation and transparent attribution of judicial conclusions; and (3) empirical evaluation demonstrates that GRM produces reasonable, robust, and human-interpretable legal reasoning outcomes even under complex precedent conflicts, significantly enhancing the explainability and trustworthiness of legal AI systems.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Precedential constraint is one foundation of case-based reasoning in AI and Law. It generally assumes that the underlying set of precedents must be consistent. To relax this assumption, a generalized notion of the reason model has been introduced. While several argumentative explanation approaches exist for reasoning with precedents based on the traditional consistent reason model, there has been no corresponding argumentative explanation method developed for this generalized reasoning framework accommodating inconsistent precedents. To address this question, this paper examines an extension of the derivation state argumentation framework (DSA-framework) to explain the reasoning according to the generalized notion of the reason model.

Problem

• Extends argumentation framework for inconsistent precedents
• Develops explanation method for generalized reason model
• Addresses reasoning with conflicting legal case precedents

Innovation

• Extends DSA-framework for generalized reason model
• Provides argumentative explanation with inconsistent precedents
• Relaxes consistency assumption in case-based reasoning