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- Title
A Bayesian model of legal syllogistic reasoning.
- Authors
Constant, Axel
- Abstract
Bayesian approaches to legal reasoning propose causal models of the relation between evidence, the credibility of evidence, and ultimate hypotheses, or verdicts. They assume that legal reasoning is the process whereby one infers the posterior probability of a verdict based on observed evidence, or facts. In practice, legal reasoning does not operate quite that way. Legal reasoning is also an attempt at inferring applicable rules derived from legal precedents or statutes based on the facts at hand. To make such an inference, legal reasoning follows syllogistic logic and first order transitivity. This paper proposes a Bayesian model of legal syllogistic reasoning that complements existing Bayesian models of legal reasoning using a Bayesian network whose variables correspond to legal precedents, statutes, and facts. We suggest that legal reasoning should be modelled as a process of finding the posterior probability of precedents and statutes based on available facts.
- Subjects
EVIDENCE; VERDICTS; BAYESIAN analysis; SYLLOGISM; LEGAL precedent; STATUTES
- Publication
Artificial Intelligence & Law, 2024, Vol 32, Issue 2, p441
- ISSN
0924-8463
- Publication type
Article
- DOI
10.1007/s10506-023-09357-8