Subtleties in Bayesian decision-theoretic analysis for forensic findings: Notes on recent discussion of the role of validation study data in rational decision making
Details
Serval ID
serval:BIB_9BD7A1D33080
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Subtleties in Bayesian decision-theoretic analysis for forensic findings: Notes on recent discussion of the role of validation study data in rational decision making
Journal
Forensic Science International: Synergy
ISSN
2589-871X
Publication state
Published
Issued date
01/09/2024
Peer-reviewed
Oui
Volume
9
Pages
100548
Language
english
Notes
The research reported in this article was supported by the Swiss Benevolent Society of New York.
Abstract
This technical note extends a recent discussion in this journal of the role of validation study data in rational decision making. One argument that has been made in this context, using elements of Bayesian decision theory, is that further aggregation of validation study data into error rates involves a loss of information that compromises rational inference and decision making and should therefore be discouraged. This technical note seeks to explain that this argument can be developed at different levels of detail, depending on the definition of the propositions of interest, the forensic findings to be evaluated (and hence the form of the likelihood ratio), and the characterization of the relative desirability of decision consequences. The analyses proposed here reveal the cascade of abstractions and assumptions into which discussions about the use of validation study results in forensic science have fallen. This reinforces the conclusion that further aggregation of validation study data into error rates is problematic. It also suggests that even if a definition of error rate(s) could be agreed upon and defensively quantified in a given application, we should rethink and possibly adjust our expectations about what exactly error rates can practically contribute to rational modes of reasoning and decision making in legal contexts.
Keywords
Bayesian decision theory, Error rates, Feature agnosticism, inconclusive, relevance, probability
Publisher's website
Open Access
Yes
Funding(s)
University of Lausanne
Other
Create date
01/09/2024 1:31
Last modification date
05/09/2024 9:10