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  1. Measuring coherence with Bayesian networks.Alicja Kowalewska & Rafal Urbaniak - forthcoming - Artificial Intelligence and Law:1-27.
    When we talk about the coherence of a story, we seem to think of how well its individual pieces fit together—how to explicate this notion formally, though? We develop a Bayesian network based coherence measure with implementation in R, which performs better than its purely probabilistic predecessors. The novelty is that by paying attention to the network structure, we avoid simply taking mean confirmation scores between all possible pairs of subsets of a narration. Moreover, we assign special importance to the (...)
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  • Narration in Judiciary Fact-Finding: A Probabilistic Explication.Rafal Urbaniak - 2018 - Artificial Intelligence and Law 26 (4):345-376.
    Legal probabilism is the view that juridical fact-finding should be modeled using Bayesian methods. One of the alternatives to it is the narration view, according to which instead we should conceptualize the process in terms of competing narrations of what happened. The goal of this paper is to develop a reconciliatory account, on which the narration view is construed from the Bayesian perspective within the framework of formal Bayesian epistemology.
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  • Calculating and Understanding the Value of Any Type of Match Evidence When There Are Potential Testing Errors.Norman Fenton, Martin Neil & Anne Hsu - 2014 - Artificial Intelligence and Law 22 (1):1-28.
    It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases (...)
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  • On Modelling Non-Probabilistic Uncertainty in the Likelihood Ratio Approach to Evidential Reasoning.Jeroen Keppens - 2014 - Artificial Intelligence and Law 22 (3):239-290.
    When the likelihood ratio approach is employed for evidential reasoning in law, it is often necessary to employ subjective probabilities, which are probabilities derived from the opinions and judgement of a human. At least three concerns arise from the use of subjective probabilities in legal applications. Firstly, human beliefs concerning probabilities can be vague, ambiguous and inaccurate. Secondly, the impact of this vagueness, ambiguity and inaccuracy on the outcome of a probabilistic analysis is not necessarily fully understood. Thirdly, the provenance (...)
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