Software Engineering Methods For AI-Driven Deductive Legal Reasoning

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Detalles Bibliográficos
Publicado en:	arXiv.org (Jun 27, 2024), p. n/a
Autor principal:	Padhye, Rohan
Publicado:	Cornell University Library, arXiv.org
Materias:	Mathematical logic Large language models Artificial intelligence Documents Reasoning Generative artificial intelligence Contract law Program verification (computers) Software engineering Automation Natural language processing Natural language Software testing
Acceso en línea:	Citation/Abstract Full text outside of ProQuest
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Descripción
Resumen:	The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the deductive rule-based reasoning inherent in statutory and contract law. This paper argues that such automated deductive legal reasoning can now be viewed from the lens of software engineering, treating LLMs as interpreters of natural-language programs with natural-language inputs. We show how it is possible to apply principled software engineering techniques to enhance AI-driven legal reasoning of complex statutes and to unlock new applications in automated meta-reasoning such as mutation-guided example generation and metamorphic property-based testing.
ISSN:	2331-8422
Fuente:	Engineering Database