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In this paper, I have two primary goals. First, I show that the causal-based story approach in A hybrid formal theory of arguments, stories and criminal evidence (or Hybrid Theory, for short) is ill suited to negative (or absence) causation. In the literature, the causal-based approach requires that hypothetical stories be causally linked to the explanandum. Many take these links to denote physical or psychological causation, or temporal precedence. However, understanding causality in those terms, as I will show, cannot capture cases of negative causation, which are of interest to the Law. In keeping with this, I also discuss some of the difficulties Hybrid Theory invites by remaining silent on the nature of the causal links. In my second aim, I sketch a way for Hybrid Theory to overcome this problem. By replacing the original, underlying causal structure with contrastive causation in the law, Hybrid Theory can represent reasoning in which the evidence that is appealed to is causally linked via negative causation to the explananda. [ABSTRACT FROM AUTHOR]

This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one story-based. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence of an event, e.g., based on witness testimony, are central. In a story-based approach, evidence is evaluated and interpreted from the perspective of the factual stories as they may have occurred in a case, e.g., as they are defended by the prosecution. In this paper, we argue that both arguments and narratives are relevant and useful in the reasoning with and interpretation of evidence. Therefore, a hybrid approach is proposed and formally developed, doing justice to both the argument-based and the narrative-based perspective. By the formalization of the theory and the associated graphical representations, our proposal is the basis for the design of software developed as a tool to make sense of the evidence in complex cases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

forensic science and forensic statistics become increasingly sophisticated, and judges and juries demand more timely delivery of more convincing scientific evidence, crime investigation is becoming progressively more challenging. In particular, this development requires more effective and efficient evidence collection strategies, which are likely to produce the most conclusive information with limited available resources. Evidence collection is a difficult task, however, because it necessitates consideration of: a wide range of plausible crime scenarios, the evidence that may be produced under these hypothetical scenarios, and the investigative techniques that can recover and interpret the plausible pieces of evidence. A knowledge based system (KBS) can help crime investigators by retrieving and reasoning with such knowledge, provided that the KBS is sufficiently versatile to infer and analyse a wide range of plausible scenarios. This paper presents such a KBS. It employs a novel compositional modelling technique that is integrated into a Bayesian model based diagnostic system. These theoretical developments are illustrated by a realistic example of serious crime investigation. [ABSTRACT FROM AUTHOR]

The present article proceeds from the mainstream view that the conceptual framework underpinning adversarial systems of criminal adjudication, i.e. a mixture of common-sense philosophy and probabilistic analysis, is unsustainable. In order to provide fact-finders with an operable structure of justification, we need to turn to epistemology once again. The article proceeds in three parts. First, I examine the structural features of justification and how various theories have attempted to overcome Agrippa's trilemma. Second, I put Inferential Contextualism to the test and show that a defeasible structure of justification allocating epistemic rights and duties to all participants of an inquiry manages to dissolve the problem of scepticism. Third, I show that our epistemic practice already embodies a contextualist mechanism. Our problem was not that our Standard of Proof is inoperable but that it was not adequately conceptualized. Contextualism provides the framework to articulate the abovementioned practice and to treat 'reasonable doubts' as a mechanism which we can now describe in detail. The seemingly insurmountable problem with our efforts to define the concept "reasonable doubts" was the fact that we have been conflating the surface features of this mechanism and its internal structure, i.e. the rules for its use. [ABSTRACT FROM AUTHOR]

Three laboratory experiments were conducted to compare legal (unquantified) definitions of three standards of proof ("preponderance of the evidence," "clear and convincing evidence," and "beyond a reasonable doubt") with quantified definitions, in which the standards of proof were expressed in probability terms (51%, 71%, and 91% probability of truth). In the first experiment, the quantified definitions had their intended effect--verdicts favoring the plaintiffs decreased as the standard of proof became stricter--but the legal definitions did not have their intended effect. These results were replicated in the second experiment; in addition, jury instructions that combined the two types of definitions (legal and quantified) did not have their intended effect. Results of the third experiment suggest that some legal definitions may be able to communicate their intended difficulty level when they appear in a comparative context. [ABSTRACT FROM AUTHOR]

The use of neuroscience in the courtroom can be traced back to the early twentieth century. However, the use of neuroscientific evidence in criminal proceedings has increased significantly over the last two decades. This rapid increase has raised questions, among the media as well as the legal and scientific communities, regarding the effects that such evidence could have on legal decision makers. In this article, we first outline the history of neuroscientific evidence in courtrooms and then we provide a review of recent research investigating the effects of neuroscientific evidence on decision-making broadly, and on legal decisions specifically. In the latter case, we review studies that measure the effect of neuroscientific evidence (both imaging and nonimaging) on verdicts, sentencing recommendations, and beliefs of mock jurors and judges presented with a criminal case. Overall, the reviewed studies suggest mitigating effects of neuroscientific evidence on some legal decisions (e.g., the death penalty). Furthermore, factors such as mental disorder diagnoses and perceived dangerousness might moderate the mitigating effect of such evidence. Importantly, neuroscientific evidence that includes images of the brain does not appear to have an especially persuasive effect (compared with other neuroscientific evidence that does not include an image). Future directions for research are discussed, with a specific call for studies that vary defendant characteristics, the nature of the crime, and a juror's perception of the defendant, in order to better understand the roles of moderating factors and cognitive mediators of persuasion. [ABSTRACT FROM AUTHOR]