Rule-based Classifier Models
This work addresses a gap in legal AI by extending classifier models to better mimic legal reasoning, though it is incremental as it builds directly on prior research.
The paper tackles the limitation of existing legal classifier models that only consider facts by incorporating rules, specifically the ratio decidendi, into a classifier framework. It demonstrates how decisions for new cases can be inferred using this enriched rule-based approach, including examples with time elements and court hierarchies.
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.