Smit Nadine M, Lagnado David A, Morgan Ruth M, Fenton Norman E
Department of Security and Crime Science, University College London, 35 Tavistock Square, London WC1H 9EZ, UK; Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London WC1H 9EZ, UK.
Department of Experimental Psychology, University College London, 26 Bedford Way, London WC1H 0AP, UK.
Crime Sci. 2016 May 25;5(1):9. doi: 10.1186/s40163-016-0057-6.
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.
定罪后若有新的法医证据出现，却没有系统的框架来帮助律师确定该证据是否对判决提出了足够多的质疑，从而发起上诉。本文提出了这样一个框架，其灵感来自于最近的一个案例。在该案例中，一名被告主要基于音频证据被定罪，但对该证据的后续分析发现了一些在审判期间未被考虑的额外声音。该框架旨在弥合科学分析中普遍已知的内容与法律环境中所假设内容之间的差距。它基于贝叶斯网络（BNs），这有可能成为在特定案例背景下评估证据的一种结构化且易于理解的方式。然而，由于过去在一些法律案件中贝叶斯网络方法的使用方式令人困惑，其在法庭上的应用遭遇了挫折。为解决这一问题，我们展示了在特定案例中推理和决策能够明确且透明的程度。贝叶斯网络方法使我们能够清晰地定义相关命题和证据，并使用敏感性分析来评估在不同假设下证据的影响。结果表明，这样一个框架适合识别当前缺失但对有效且完整的推理过程显然至关重要的信息。此外，还提供了一种方法，通过该方法贝叶斯网络不仅可以作为法医案件中依据不完整证据进行推理的指南，还能识别出非常具体的研究问题，这些问题应得到解决以扩展证据基础并在未来解决类似问题。
