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The hierarchy of propositions has been accepted amongst the forensic science community for some time. It is also accepted that the higher up the hierarchy the propositions are, against which the scientist are competent to evaluate their results, the more directly useful the testimony will be to the court. Because each case represents a unique set of circumstances and findings, it is difficult to come up with a standard structure for evaluation. One common tool that assists in this task is Bayesian networks (BNs). There is much diversity in the way that BN can be constructed. In this work, we develop a template for BN construction that allows sufficient flexibility to address most cases, but enough commonality and structure that the flow of information in the BN is readily recognised at a glance. We provide seven steps that can be used to construct BNs within this structure and demonstrate how they can be applied, using a case example.

In court, it is typical for biological evidence to be reported at a level that only addresses how likely the DNA evidence is if it originated from a particular individual, or individuals. However, there are other questions that could be considered that would be of value in enabling the court, including the jury, to make better informed decisions. For example, although answers to specific questions such as: “Which type of bodily fluid has the DNA originated from?” or, “How was the DNA deposited at the scene?” would be probabilistic in nature, they can be crucial to the outcome of a case. The relationship between the DNA evidence, the source of the DNA and the activity that took place is described in a term called the “hierarchy of propositions.” Currently, such questions are usually answered by scientists subjectively with little to no logical framework to assist them. Bayesian networks have proven to be beneficial in providing logical reasoning by way of a likelihood ratio to help combine subjective, yet, experience‐based, opinions of experts with experimental data when answering questions which can be both complex and uncertain. These networks offer a framework that provides balance, transparency, and robustness in the evaluation of evidence. A current limitation of the use of Bayesian networks includes a lack of understanding of the underlying concepts from both forensic scientists and the courts and consequently a reduced recognition of the potential strengths.

When new forensic evidence becomes available after a conviction there is no systematic framework to help lawyers to determine whether it raises sufficient questions about the verdict in order to launch an appeal. This paper presents such a framework driven by a recent case, in which a defendant was convicted primarily on the basis of audio evidence, but where subsequent analysis of the evidence revealed additional sounds that were not considered during the trial. The framework is intended to overcome the gap between what is generally known from scientific analyses and what is hypothesized in a legal setting. It is based on Bayesian networks (BNs) which have the potential to be a structured and understandable way to evaluate the evidence in a specific case context. However, BN methods suffered a setback with regards to the use in court due to the confusing way they have been used in some legal cases in the past. To address this concern, we show the extent to which the reasoning and decisions within the particular case can be made explicit and transparent. The BN approach enables us to clearly define the relevant propositions and evidence, and uses sensitivity analysis to assess the impact of the evidence under different assumptions. The results show that such a framework is suitable to identify information that is currently missing, yet clearly crucial for a valid and complete reasoning process. Furthermore, a method is provided whereby BNs can serve as a guide to not only reason with incomplete evidence in forensic cases, but also identify very specific research questions that should be addressed to extend the evidence base and solve similar issues in the future.