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A formal approach to qualifying and quantifying the ‘goodness’ of forensic identification decisions
Law, Probability and Risk ( IF 1.4 ) Pub Date : 2018-08-04 , DOI: 10.1093/lpr/mgy016
Alex Biedermann 1 , Silvia Bozza 1, 2 , Franco Taroni 1 , Paolo Garbolino 3
Affiliation  

In this article, we review and analyse common understandings of the degree to which forensic inference of source—also called identification or individualization—can be approached with statistics and is referred to, increasingly often, as a decision. We also consider this topic from the strongly empirical perspective of PCAST (2016) in its recent review of forensic science practice. We will point out why and how these views of forensic identification as a decision, and empirical approaches to it (namely experiments by multiple experts under controlled conditions), provide only descriptive measures of expert performance and of general scientific validity regarding particular forensic branches (e.g. fingermark examination). Although relevant to help assess whether the identification practice of a given forensic field can be trusted, these empirical accounts do not address the separate question of what ought to be a sensible, or ‘good’ in some sense, (identification-)decision to make in a particular case. The latter question, as we will argue, requires additional considerations, such as decision-making goals. We will point out that a formal approach to qualifying and quantifying the relative merit of competing forensic decisions can be considered within an extended view of statistics in which data analysis and inference are a necessary but not sufficient preliminary.

中文翻译:

一种对法医鉴定决定的“优点”进行限定和量化的正式方法

在本文中,我们回顾和分析了对来源的法医推断(也称为识别或个性化)可以通过统计进行处理的程度的共同理解,并且越来越多地被称为决策。我们还在 PCAST​​(2016)最近对法医学实践的回顾中从强烈的实证角度考虑了这个话题。我们将指出为什么以及如何将法医鉴定作为决策的这些观点以及对其的实证方法(即在受控条件下由多名专家进行的实验)仅提供专家表现和关于特定法医分支的一般科学有效性的描述性措施(例如指纹检查)。尽管有助于评估特定法医领域的识别实践是否可信,这些经验说明并没有解决在某种意义上应该是明智的或“好的”的单独问题,即在特定情况下做出的(识别)决定。正如我们将要讨论的,后一个问题需要额外的考虑,例如决策目标。我们将指出,可以在统计的扩展视图中考虑对竞争取证决定的相对优点进行限定和量化的正式方法,其中数据分析和推理是必要的但不充分的初步。
更新日期:2018-08-04
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