Bayesian networks and chain event graphs as decision making tools in forensic science

Gail Robertson (University of Edinburgh)


Abstract:
Bayes' theorem and likelihood ratios are used in forensic statistics to compare evidence supporting different propositions put forward during court proceedings. There is widespread interest among forensic scientists in using Bayesian network models to evaluate the extent to which scientific evidence supports hypotheses proposed by the prosecution and defence. Bayesian networks are frequently used to compare support for source-level propositions, e.g. propositions concerned with determining the source of samples found at crime scenes such as hair, fibres, and DNA. While comparing source-level propositions is useful, propositions which refer to criminal activities (i.e. those concerned with understanding how a sample came to be at the crime scene) are of increasing interest to courts. Less work has been done on developing probabilistic methods to assess activity-level propositions, hence finding a method of evaluating evidence for these types of propositions would benefit practitioners. Chain event graphs have been proposed as a decision making tool to assess the extent to which evidence supports event timelines put forward by the prosecution and defence, and may be a useful method for assessing activity-level propositions. In this study we used Bayesian networks and chain event graphs to combine different types of evidence supporting activity-level propositions from a real-world drug trafficking case. We compared the use of Bayesian networks and chain event graphs in evaluating evidence to support activity-level propositions associated with the case, and demonstrate how graphical methods can be used to evaluate the extent to which evidence from a drug trafficking case supports prosecution or defence propositions.

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