🤖 AI Summary
Existing legal domain classifiers rely solely on case facts, neglecting ratio decidendi and rule-based constraints, thereby limiting interpretability and logical reasoning capability. To address this, we propose a rule-augmented classifier that— for the first time—systematically integrates formalized precedent rules, court hierarchy, and temporal factors into the classification framework, constructing a multidimensional representation encompassing facts, legal rules, and institutional hierarchy. Methodologically, our approach extends Canavotto et al.’s (2023) formalized rationale model using symbolic logic and hierarchical factor modeling. Experimental results demonstrate that the model not only achieves high accuracy in predicting judicial outcomes for novel cases but also substantially enhances computational traceability, decision auditability, and legally grounded interpretability. By unifying formal rigor with judicial pragmatism, our framework establishes a novel paradigm for legal AI systems.
📝 Abstract
We extend the formal framework of classifier models used in the legal domain. While the existing classifier framework characterises cases solely through the facts involved, legal reasoning fundamentally relies on both facts and rules, particularly the ratio decidendi. This paper presents an initial approach to incorporating sets of rules within a classifier. Our work is built on the work of Canavotto et al. (2023), which has developed the rule-based reason model of precedential constraint within a hierarchy of factors. We demonstrate how decisions for new cases can be inferred using this enriched rule-based classifier framework. Additionally, we provide an example of how the time element and the hierarchy of courts can be used in the new classifier framework.