A Method for Explaining Bayesian Networks for Legal Evidence with Scenarios

Charlotte S. Vlek, Henry Prakken, Silja Renooij, Bart Verheij

In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study.

Manuscript (in PDF-format)

Reference:
Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2016). A Method for Explaining Bayesian Networks for Legal Evidence with Scenarios. Artificial Intelligence and Law 24 (3), 285-324. http://dx.doi.org/10.1007/s10506-016-9183-4.

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