Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

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

In a criminal trial, a judge or jury needs to reach a conclusion about `what happened' based on the available evidence. Of- ten this includes probabilistic evidence. Whereas Bayesian networks form a good tool for analysing evidence proba- bilistically, simply presenting the outcome of the network to a judge or jury does not allow them to make an informed decision. In this paper, we propose to combine Bayesian networks with a narrative approach to reasoning with legal evidence, the result of which allows a juror to reason with alternative scenarios while also incorporating probabilistic information. The proposed method aids both the construc- tion and the understanding of Bayesian networks, using sce- nario schemes. We make three distinct contributions: (1) we propose to use scenario schemes to aid the construction of Bayesian networks, (2) we propose a method for producing scenarios in text form from the resulting networks and (3) we propose a format for reporting the alternative scenarios and their relations to the evidence (including strength).

Manuscript (in PDF-format)

Reference:
Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2015). Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. The 15th International Conference on Artificial Intelligence and Law (ICAIL 2015). Proceedings of the Conference, 128-137. New York (New York): ACM.

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