A formal approach to qualifying and quantifying the ‘goodness' of forensic identification decisions

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Version: Author's accepted manuscript
Serval ID
serval:BIB_EB853E42C84E
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A formal approach to qualifying and quantifying the ‘goodness' of forensic identification decisions
Journal
Law, Probability and Risk
Author(s)
Biedermann Alex, Bozza Silvia, Taroni Franco, Garbolino Paolo
ISSN
1470-8396
1470-840X
Publication state
Published
Issued date
01/12/2018
Peer-reviewed
Oui
Volume
17
Number
4
Pages
295-310
Language
english
Notes
This research was supported by the Swiss National Science Foundation through grant No. BSSGI0_155809 and the University of Lausanne.
Abstract
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.
Keywords
forensic individualization, decision analysis, decision theory, minimax, decision-theoretic criterion
Create date
16/12/2018 21:20
Last modification date
20/08/2019 16:13
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