Rule-based Classifier Models
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.
Cite
@article{arxiv.2505.00474,
title = {Rule-based Classifier Models},
author = {Cecilia Di Florio and Huimin Dong and Antonino Rotolo},
journal= {arXiv preprint arXiv:2505.00474},
year = {2025}
}
Comments
11 pages, 1 figure. Extended version of a short paper accepted to ICAIL 2025. This is the authors' version of the work. It is posted here for your personal use