Beyond Logic Programming for Legal Reasoning Article Swipe
Ha-Thanh Nguyen
,
Francesca Toni
,
Kostas Stathis
,
Ken Satoh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.16632
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.16632
Logic programming has long being advocated for legal reasoning, and several approaches have been put forward relying upon explicit representation of the law in logic programming terms. In this position paper we focus on the PROLEG logic-programming-based framework for formalizing and reasoning with Japanese presupposed ultimate fact theory. Specifically, we examine challenges and opportunities in leveraging deep learning techniques for improving legal reasoning using PROLEG identifying four distinct options ranging from enhancing fact extraction using deep learning to end-to-end solutions for reasoning with textual legal descriptions. We assess advantages and limitations of each option, considering their technical feasibility, interpretability, and alignment with the needs of legal practitioners and decision-makers. We believe that our analysis can serve as a guideline for developers aiming to build effective decision-support systems for the legal domain, while fostering a deeper understanding of challenges and potential advancements by neuro-symbolic approaches in legal applications.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.16632
- https://arxiv.org/pdf/2306.16632
- OA Status
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- 10
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All OpenAlex metadata
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https://openalex.org/W4382765595Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.16632Digital Object Identifier
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Beyond Logic Programming for Legal ReasoningWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
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2023-06-29Full publication date if available
- Authors
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Ha-Thanh Nguyen, Francesca Toni, Kostas Stathis, Ken SatohList of authors in order
- Landing page
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https://arxiv.org/abs/2306.16632Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2306.16632Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2306.16632Direct OA link when available
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Computer science, Interpretability, Artificial intelligence, Management science, EngineeringTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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