Although the use of statistics in legal proceedings has considerably grown in the last 40 years, primarily classical statistical methods rather than Bayesian methods have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This article reviews the potential and actual use of Bayes in the law and explains the main reasons for its lack of impact on legal practice. These reasons include misconceptions by the legal community about Bayes' theorem, overreliance on the use of the likelihood ratio, and the lack of adoption of modern computational methods. We argue that Bayesian networks, which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using Bayes in the law.
The likelihood ratio (LR) is a probabilistic method that has been championed as a 'simple rule' for evaluating the probative value of forensic evidence in court. Intuitively, if the LR is greater than one then the evidence supports the prosecution hypothesis; if the LR is less than one it supports the defence hypothesis, and if the LR is equal to one then the evidence favours neither (and so is considered 'neutral' -having no probative value). It can be shown by Bayes' theorem that this simple relationship only applies to pairs of hypotheses for which one is the negation of the other (i.e. to mutually exclusive and exhaustive hypotheses) and is not applicable otherwise. We show how easy it can be -even for evidence experts -to use pairs of hypotheses that they assume are mutually exclusive and exhaustive but are not, and hence to arrive at erroneous conclusions about the value of evidence using the LR. Furthermore, even when mutually exclusive and exhaustive hypotheses are used there are extreme restrictions as to what can be concluded about the probative value of evidence just from a LR. Most importantly, while the distinction between source-level hypotheses (such as defendant was/was not at the crime scene) and offence-level hypotheses (defendant is/is not guilty) is well known, it is not widely understood that a LR for evidence about the former generally has no bearing on the LR of the latter. We show for the first time (using Bayesian networks) the full impact of this problem, and conclude that it is only the LR of the offence level hypotheses that genuinely determine the probabitive value of the evidence. We investigate common scenarios in which evidence has a LR of one but still has significant probative value (i.e. is not neutral as is commonly assumed). As illustration we consider the ramifications of these points for the case of Barry George. The successful appeal against his conviction for the murder of Jill Dando was based primarily on the argument that the firearm discharge residue (FDR) evidence, assumed to support the prosecution hypothesis at the original trial, actually had an LR equal to one and hence was 'neutral'. However, our review of the appeal transcript shows numerous examples of the problems with the use of hypotheses identified above. We show that if one were to follow the arguments recorded in the Appeal judgment verbatim, then contrary to the Appeal conclusion, the probative value of the FDR evidence may not have been neutral as was concluded.
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