Context effect and confirmation bias in criminal fact finding

Purpose: Fact finding is an important part of the job of criminal trial judges and juries. In the literature, several potential pitfalls hindering fact finding have been identified, such as context effects (i.e. an unintended effect of non-probative information on conviction) and confirmation bias (i.e. a skewed selection of and overreliance on guilt-confirming evidence and neglect of exonerating information). In the present study, the effect of irrelevant contextual information on conviction and subsequent confirmation bias was tested. Method: A sample of Dutch professional criminal trial judges (N = 105) studied a case file and decided on their conviction of the suspect’s guilt, and subsequent investigation endeavours. There were two versions of the file, differing in non-probative details that might affect conviction, such as crime severity and facial appearance of the suspect. Results: Findings suggest that context information indeed affected conviction, and the subsequent preference for guilt-confirming investigation endeavours. Conclusion: Professional judges may be susceptible to bias threatening the objectivity of legal decision-making

Shared Evidence: It all depends…

When reasoning about evidence, we must carefully consider the impact of different structures. For instance, if in the process of evaluating multiple reports, we find they rely on the same, shared evidence, then the support proffered by those reports is dependent on that evidence. Critically, normative accounts suggest that such a dependency results in redundant information across reports (reducing evidential support), relative to reports based on distinct items of evidence. In the present work we disentangle the structural and observation-based indicators of this form of dependency. In so doing, we present novel findings that lay reasoners are not only insensitive to shared evidence structures when updating their beliefs, but also that reasoners do not necessarily prefer more diverse sources of evidence. Finally, we replicate prior effects in reasoning under uncertainty, including conservative sequential updating, and difficulty in integrating contradictory reports

Source reliability and the continued influence effect of misinformation: A Bayesiannetwork approach

Misinformation, and its impact on society, has become anincreasingly topical field of study of late. A body of literatureexists that suggests misinformation can retain an influenceover beliefs despite subsequent retraction, known as theContinued Influence Effect (CIE). Researchers have arguedthis to be irrational. However, we show using a Bayesianformalism why this argument is overly assumptive, pointingto (previously overlooked) considerations of reliability of, anddependence between, misinforming and retracting sources.We demonstrate that lay reasoners intuitively endorseassumptions that demarcate CIE as a rational process, basedon the fact misinformation precedes its retraction. Moreover,despite using established CIE materials, we further upturn theapplecart by finding participants show CIE, and appropriatelypenalize the reliabilities of contradicting sources

Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden

Several countries are currently investigating issues of neglect, poor quality care and abuse in the aged care sector. In most cases it is the State who license and monitor aged care providers, which frequently introduces a serious conflict of interest because the State also operate many of the facilities where our most vulnerable peoples are cared for. Where issues are raised with the standard of care being provided, the State are seen by many as a deep-pockets defendant and become the target of high-value lawsuits. This paper draws on cases and circumstances from one jurisdiction based on the English legal tradition, Australia, and proposes a Bayesian solution capable of determining probability for success for citizen plaintiffs who bring negligence claims against a public authority defendant. Use of a Bayesian network trained on case audit data shows that even when the plaintiff case meets all requirements for a successful negligence litigation, success is not often assured. Only in around one-fifth of these cases does the plaintiff succeed against a public authority as defendant

Calculating and understanding the value of any type of match evidence when there are potential testing errors

It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases of such features, a serious challenge to the use of Bayes in such legal contexts is that its standard formulaic representations are not readily understandable to non-statisticians. Attempts to get round this problem usually involve representations based around some variation of an event tree. While this approach works well in explaining the most trivial instance of Bayes’ theorem (involving a single hypothesis and a single piece of evidence) it does not scale up to realistic situations. In particular, even with a single piece of match evidence, if we wish to incorporate the possibility that there are potential errors (both false positives and false negatives) introduced at any stage in the investigative process, matters become very complex. As a result we have observed expert witnesses (in different areas of speciality) routinely ignore the possibility of errors when presenting their evidence. To counter this, we produce what we believe is the first full probabilistic solution of the simple case of generic match evidence incorporating both classes of testing errors. Unfortunately, the resultant event tree solution is too complex for intuitive comprehension. And, crucially, the event tree also fails to represent the causal information that underpins the argument. In contrast, we also present a simple-to-construct graphical Bayesian Network (BN) solution that automatically performs the calculations and may also be intuitively simpler to understand. Although there have been multiple previous applications of BNs for analysing forensic evidence—including very detailed models for the DNA matching problem, these models have not widely penetrated the expert witness community. Nor have they addressed the basic generic match problem incorporating the two types of testing error. Hence we believe our basic BN solution provides an important mechanism for convincing experts—and eventually the legal community—that it is possible to rigorously analyse and communicate the full impact of match evidence on a case, in the presence of possible error

How to model mutually exclusive events based on independent causal pathways in Bayesian network models

This is supported by ERC project ERC-2013-AdG339182-BAYES_KNOWLEDGE

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

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.This 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)

From the brain to the field: The applications of social neuroscience to economics, health, and law

Social neuroscience aims to understand the biological systems that underlie people’s thoughts, feelings and actions in light of the social context in which they operate. Over the past few decades, social neuroscience has captured the interest of scholars, practitioners, and experts in other disciplines, as well as the general public who more and more draw upon the insights and methods of social neuroscience to explain, predict and change behavior. With the popularity of the field growing, it has become increasingly important to consider the validity of social neuroscience findings as well as what questions it can and cannot address. In the present review article, we examine the contribution of social neuroscience to economics, health, and law, three domains with clear societal relevance. We address the concerns that the extrapolation of neuroscientific results to applied social issues raises within each of these domains, and we suggest guidelines and good practices to circumvent these concerns