Using Bayesian networks to guide the assessment of new evidence in an appeal case

Abstract

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.

Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events

“…The proposed modeling approach is also a contribution to the literature on Bayesian network modeling in law because it is one of very few addressing civil litigation. Almost all Bayesian network papers in law have focused on criminal law, with special emphasis on assessing evidence [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. A major methodological difference is that much of the modeling in criminal cases is focused on computing likelihood ratios for competing theories of a legal case [53], while the method proposed here focuses on computing the probability of failing to meet a generally accepted standard.…”

A cultural change to enable improved decision-making in forensic science: A six phased approach

“…To date, the requirements of both the legal and scientific domains have been addressed by developing the evidence base behind the behaviour of forensic traces through empirical research in many different fields within forensic science based upon the abundance of properties, the effects of evidence dynamics, the processes affecting collection and analysis methods, and by developing methods to assist in drawing justifiable and transparent inferences [87,86]. However, it is crucial to also identify each decision, and the interactions of that decision with other decisions made, the stakeholders in the process, along with this evidence base, at each stage from crime scene to court (Figure 3), Methodologies from the ergonomics and human factors literature [69,70,71] can be of benefit in achieving this aim, as set out in Phase 1.…”

Widening Access to Bayesian Problem Solving

“…Bayesian networks are finding increasing use in applied domains requiring people to make complex predictions and decisions on the basis of a range of uncertain and interconnected factors, ranging from forensic (Smit et al, 2016) over medical (Fenton and Neil, 2010;Constantinou et al, 2016) to meteorological contexts (Boneh et al, 2015). However, until now these methods have largely remained accessible only to experts in Bayesian probability theory or practitioners with extensive training (Nicholson et al, 2011;Smit et al, 2016).…”