# Modelling and Explaining Legal Case-based Reasoners through Classifiers

arXiv: https://arxiv.org/abs/2210.11217 · HTML: https://mlsanity.com/paper/2210.11217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018)
- [On The Reasons Behind Decisions](https://mlsanity.com/paper/2002.09284/) (2020)
- [On the Computational Intelligibility of Boolean Classifiers](https://mlsanity.com/paper/2104.06172/) (2021)

## Built on by (most cited first, 3 of 3)

- [Stable Normative Explanations: From Argumentation to Deontic Logic](https://mlsanity.com/paper/2307.05156/) (2023)
- [Rule-based Classifier Models](https://mlsanity.com/paper/2505.00474/) (2025)
- [A Modal Logic for Temporal and Jurisdictional Classifier Models](https://mlsanity.com/paper/2510.13691/) (2025)

## Abstract (arXiv)

This paper brings together two lines of research: factor-based models of case-based reasoning (CBR) and the logical specification of classifiers. Logical approaches to classifiers capture the connection between features and outcomes in classifier systems. Factor-based reasoning is a popular approach to reasoning by precedent in AI & Law. Horty (2011) has developed the factor-based models of precedent into a theory of precedential constraint. In this paper we combine the modal logic approach (binary-input classifier, BLC) to classifiers and their explanations given by Liu & Lorini (2021) with Horty's account of factor-based CBR, since both a classifier and CBR map sets of features to decisions or classifications. We reformulate case bases of Horty in the language of BCL, and give several representation results. Furthermore, we show how notions of CBR, e.g. reason, preference between reasons, can be analyzed by notions of classifier system.