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On modelling non-probabilistic uncertainty in the likelihood ratio approach to evidential reasoning

On modelling non-probabilistic uncertainty in the likelihood ratio approach to evidential reasoning 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 (expert). 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 of subjective probabilities and the associated potential sources of vagueness, ambiguity and inaccuracy tend to be poorly understood, making it difficult for the outcome of probabilistic reasoning to be explained and validated, which is crucial in legal applications. The former two concerns have been addressed by a wide body of research in AI. The latter, however, has received little attention. This paper presents a novel approach to employ argumentation to reason about probability distributions in probabilistic models. It introduces a range of argumentation schemes and corresponding sets of critical questions for the construction and validation of argument models that define sets of probability distributions. By means of an extended example, the paper demonstrates how the approach, argumentation schemes and critical questions can be employed for the development of models and their validation in legal applications of the likelihood ratio approach to evidential reasoning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

On modelling non-probabilistic uncertainty in the likelihood ratio approach to evidential reasoning

Artificial Intelligence and Law , Volume 22 (3) – Apr 24, 2014

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Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Computer Science; Artificial Intelligence (incl. Robotics); International IT and Media Law, Intellectual Property Law; Philosophy of Law; Legal Aspects of Computing; Information Storage and Retrieval
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1007/s10506-014-9157-3
Publisher site
See Article on Publisher Site

Abstract

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 (expert). 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 of subjective probabilities and the associated potential sources of vagueness, ambiguity and inaccuracy tend to be poorly understood, making it difficult for the outcome of probabilistic reasoning to be explained and validated, which is crucial in legal applications. The former two concerns have been addressed by a wide body of research in AI. The latter, however, has received little attention. This paper presents a novel approach to employ argumentation to reason about probability distributions in probabilistic models. It introduces a range of argumentation schemes and corresponding sets of critical questions for the construction and validation of argument models that define sets of probability distributions. By means of an extended example, the paper demonstrates how the approach, argumentation schemes and critical questions can be employed for the development of models and their validation in legal applications of the likelihood ratio approach to evidential reasoning.

Journal

Artificial Intelligence and LawSpringer Journals

Published: Apr 24, 2014

References