Journal
ARTIFICIAL INTELLIGENCE AND LAW
Volume 27, Issue 4, Pages 403-430Publisher
SPRINGER
DOI: 10.1007/s10506-019-09250-3
Keywords
Bayesian model comparison and averaging; Bayesian networks; Legal argumentation
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Funding
- European Research Council [ERC-2013-AdG339182-BAYES_KNOWLEDGE]
- Isaac Newton Institute for Mathematical Sciences (EPSRC) [EP/K032208/1]
- Isaac Newton Institute for Mathematical Sciences (Simons Foundation)
- Leverhulme Trust [RPG-2016-118 CAUSAL-DYNAMICS]
- EPSRC [EP/K032208/1] Funding Source: UKRI
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Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and 'average' Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational.
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