A Modal Logic for Temporal and Jurisdictional Classifier Models
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912649845932032 |
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| author | Di Florio, Cecilia Dong, Huimin Rotolo, Antonino |
| author_facet | Di Florio, Cecilia Dong, Huimin Rotolo, Antonino |
| contents | Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Modal Logic for Temporal and Jurisdictional Classifier Models Di Florio, Cecilia Dong, Huimin Rotolo, Antonino Artificial Intelligence Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system. |
| title | A Modal Logic for Temporal and Jurisdictional Classifier Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.13691 |