Bayesian network analysis of evidence in criminal court cases

Abstract

When justice goes wrong, grave consequences entail. They are damaging to the standing of the legal system and people’s lives. Humans are not good at assessing uncertainties. Parties to a legal proceeding adduce evidence to support or reject hypotheses. Errors happen when the tribunal fails to consider properly all the evidence in the context of inherent probabilities or improbabilities.

This research work does not advocate trials by mathematics or statistics. This work extends the understanding of the application of Bayesian Networks in the law domain. The original contribution to knowledge is the analysis of evidence by Bayesian Network in the context of specific legal requirements of Hong Kong.

The research questions are:

1. What are the legal requirements for the analysis of evidence in a criminal trial in Hong Kong? 2. How can a Bayesian Network be constructed for the purpose of such analysis? 3. Is such a Bayesian Network effective for the analysis?

In answering the questions, this research work examined the feasibility of generic models created for digital crime scene investigations and concluded that each case must be, for the purpose of analysis of evidence in the trial, represented by a different Bayesian Network.

This research work examined the trial processes, the tasks of tribunal of facts of criminal trials and some appellate decisions in Hong Kong. The work also created models of reasoning processes for the juries in Hong Kong. The work then compared the properties of Bayesian Networks with the processes of evaluation of evidence during trials.

This research work also considered the reluctance of courts in the United Kingdom to allow experts to express their opinions on the bases of Bayesian calculations; even though trials are practically evaluations of uncertainties and assignments of degrees of beliefs. This research work then constructed a schedule of levels of proof and proposed a schematic method, to be used with the schedule, to construct Bayesian Networks for any types of trial in Hong Kong. The method requires an analyst to go through a mass of evidence systematically, and analyse their relationships amongst the ultimate probandum, the penultimate probanda, the intermediate propositions, the facts in issue and the facts relevant to the issue. This work then demonstrated the applications by two criminal cases in Hong Kong.

The analyses show that the construction of Bayesian Network by the schematic method enables an analyst to take precaution to reach an assessment rationally and to approximate as far as capable his or her belief to the facts in

issue.