Normative decision analysis in forensic science

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Version: Author's accepted manuscript
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Serval ID
serval:BIB_CBB1183032A2
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Normative decision analysis in forensic science
Journal
Artificial Intelligence and Law
Author(s)
Biedermann Alex, Bozza Silvia, Taroni Franco
ISSN
0924-8463
1572-8382
Publication state
Published
Issued date
03/2020
Peer-reviewed
Oui
Volume
28
Number
1
Pages
7-25
Language
english
Abstract
This paper focuses on the normative analysis—in the sense of the classic decision-theoretic formulation—of decision problems that arise in connection with forensic expert reporting. We distinguish this analytical account from other common types of decision analyses, such as descriptive approaches. While decision theory is, since several decades, an extensively discussed topic in legal literature, its use in forensic science is more recent, and with an emphasis on goals such as the analysis of the logical structure of forensic expert conclusions regarding, for example, propositions of common source of evidential and known materials. Typical examples are so-called identification (or, individualization) decisions, especially categorical conclusions according to which fingermarks (or stains of biological nature, handwriting, etc.) come from a particular a person of interest. We will present and compare ways of stating forensic identification decisions in decision-theoretic terms and explain their underlying rationale. In particular, we will emphasize the importance of viewing this analysis as normative in the sense of providing a reflective rather than a prescriptive reference point against which people in charge of forensic identification decisions may compare their otherwise (possibly) intuitive and informal reasoning, before acting. Normative decision analysis in forensic science thus provides a vector through which current practice can be articulated, scrutinized and rethought.
Keywords
Law, Artificial Intelligence
Funding(s)
Swiss National Science Foundation / BSSGI0_155809
Create date
12/03/2020 2:19
Last modification date
12/03/2020 8:10
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