Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

In a criminal trial, a judge or jury needs to reach a conclusionabout ‘what happened’ based on the available evidence. Oftenthis includes probabilistic evidence. Whereas Bayesiannetworks form a good tool for analysing evidence probabilistically,simply presenting the outcome of the network toa judge or jury does not allow them to make an informeddecision. In this paper, we propose to combine Bayesiannetworks with a narrative approach to reasoning with legalevidence, the result of which allows a juror to reason withalternative scenarios while also incorporating probabilisticinformation. The proposed method aids both the constructionand the understanding of Bayesian networks, using scenarioschemes. We make three distinct contributions: (1) wepropose to use scenario schemes to aid the construction ofBayesian networks, (2) we propose a method for producingscenarios in text form from the resulting networks and (3)we propose a format for reporting the alternative scenariosand their relations to the evidence (including strength)