a paper published today by Nadine Smit and colleagues in Crime Science presents such a framework driven by a recent case, in which a defendant was convicted primarily on the basis of sound evidence, but where subsequent analysis of the evidence revealed additional sounds that were not considered during the trial.
From the case documentation, we know the following:
- A baby was injured during an incident on the top floor of a house
- Blood from the baby was found on the wall in one of the rooms upstairs
- On an audio recording of the emergency telephone call made by the suspect, a scraping sound (allegedly indicating scraping blood off a wall) can be heard
- The suspect was charged with attempted murder
The framework described in Smit's paper 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 are a structured and understandable way to evaluate the evidence in the specific case context and present it in a clear manner in court. However, BN methods are often criticised for not being sufficiently transparent for legal professionals. To address this concern the paper shows the extent to which the reasoning and decisions of 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 prior assumptions. The results show that such a framework is suitable to identify information that is currently missing, and 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 to solve similar issues in the future.
Smit, N. M., Lagnado, D. A., Morgan, R. M., & Fenton, N. E. (2016). "An investigation of the application of Bayesian networks to case assessment in an appeal case". Crime Science, 2016, 5: 9, DOI 10.1186/s40163-016-0057-6 (open source). Published version pdf.The research was funded by the Engineering and Physical Sciences Research Council of the UK through the Security Science Doctoral Research Training Centre (UCL SECReT) based at University College London (EP/G037264/1), and the European Research Council (ERC-2013-AdG339182-BAYES_KNOWLEDGE).
The BN model (which is fully spceified in the paper) was built and run using the free version of AgenaRisk.