<div class="csl-bib-body">
<div class="csl-entry">Di Florio, C., Dong, H., & Rotolo, A. (2026). Rule-based Classifier Models. In J. Maranhão (Ed.), <i>ICAIL ’25: Proceedings of the Twentieth International Conference on Artificial Intelligence and Law</i> (pp. 465–469). ACM. https://doi.org/10.1145/3769126.3769243</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/225527
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dc.description.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
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dc.language.iso
en
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dc.subject
Legal Case Based Reasoning
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dc.subject
Legal Classifiers
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dc.subject
Rule Based Reasoning
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dc.subject
Factor Hierarchies
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dc.title
Rule-based Classifier Models
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of Bologna, Italy
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dc.contributor.affiliation
University of Bologna, Italy
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dc.relation.isbn
979-8-4007-1939-4
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dc.relation.doi
10.1145/3769126
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dc.description.startpage
465
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dc.description.endpage
469
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
ICAIL '25: Proceedings of the Twentieth International Conference on Artificial Intelligence and Law
