The distinction between discriminability and reliability in forensic science.

Bayesian Discriminability Forensic science ROC analysis Reliability Signal detection theory

Journal

Science & justice : journal of the Forensic Science Society
ISSN: 1876-4452
Titre abrégé: Sci Justice
Pays: England
ID NLM: 9508563

Informations de publication

Date de publication:
07 2021
Historique:
received: 07 12 2020
revised: 15 03 2021
accepted: 23 04 2021
entrez: 26 6 2021
pubmed: 27 6 2021
medline: 15 4 2022
Statut: ppublish

Résumé

Forensic science plays an increasingly important role in the criminal justice system; yet, many forensic procedures have not been subject to the empirical scrutiny that is expected in other scientific disciplines. Over the past two decades, the scientific community has done well to bridge the gap, but have likely only scratched the tip of the iceberg. We offer the discriminability-reliability distinction as a critical framework to guide future research on diagnostic-testing procedures in the forensic science domain. We argue that the primary concern of the scientist ought to be maximizing discriminability and that the primary concern of the criminal justice system ought to be assessing the reliability of evidence. We argue that Receiver Operating Characteristic (ROC) analysis is uniquely equipped for determining which of two procedures or conditions has better discriminability and we also demonstrate how estimates of reliability can be extracted from this Signal Detection framework.

Identifiants

pubmed: 34172120
pii: S1355-0306(21)00043-5
doi: 10.1016/j.scijus.2021.04.002
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

319-331

Informations de copyright

Copyright © 2021 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Auteurs

Andrew M Smith (AM)

Iowa State University, United States. Electronic address: amsmith@iastate.edu.

Tess M S Neal (TMS)

Arizona State University, United States.

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Classifications MeSH