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Using Chain Event Graphs to Address Asymmetric Evidence in Legal Reasoning

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FOSW02 - Bayesian networks and argumentation in evidence analysis

Co-author: James Q. Smith (University of Warwick)

Bayesian networks (BNs), a class of probabilistic graphical models, have been useful in providing a graphical representation of a problem, calculating marginal and conditional probabilities of interest, and making inferences particularly addressing propositions about the source or an evidential-sample. To address propositions relating to activities, there is a need to account for different plausible explanations of a suspect/perpetrator’s actions and events as it relates to the evidence. We propose the use of another class of graphical models, chain event graphs (CEGs), exploiting event tree structures to depict the unfolding events as postulated by each side (defence and prosecution) and differing explanations/scenarios. Different explanations/scenarios can introduce different sets of relevant information affecting the dependence relationship between variables and symmetry of the structure. With the use of case examples involving transfer and persistence and different evidence types (but in which DNA provides a sub-source level of attribution), we further show how CEGs can assist in the careful pairing and development of propositions and analysis of the evidence by addressing uncertainty and the asymmetric unfolding of the events to better assist the courts.

This talk is part of the Isaac Newton Institute Seminar Series series.

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