Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science.
EWG, Expert Working Group on Human Factors in Latent Print Analysis
FSR, Forensic Science Regulator for England & Wales
Forensic data science
Forensic science
HoL, House of Lords Science and Technology Select Committee
Likelihood ratio
PCAST, President’s Council of Advisors on Science and Technology
Paradigm shift
TRL, technology readiness level
UKRI, United Kingdom Research and Innovation
Validation
Journal
Forensic science international. Synergy
ISSN: 2589-871X
Titre abrégé: Forensic Sci Int Synerg
Pays: Netherlands
ID NLM: 101766849
Informations de publication
Date de publication:
2022
2022
Historique:
received:
15
02
2022
revised:
06
05
2022
accepted:
16
05
2022
entrez:
31
5
2022
pubmed:
1
6
2022
medline:
1
6
2022
Statut:
epublish
Résumé
Widespread practice across the majority of branches of forensic science uses analytical methods based on human perception, and interpretive methods based on subjective judgement. These methods are non-transparent and are susceptible to cognitive bias, interpretation is often logically flawed, and forensic-evaluation systems are often not empirically validated. I describe a paradigm shift in which existing methods are replaced by methods based on relevant data, quantitative measurements, and statistical models; methods that are transparent and reproducible, are intrinsically resistant to cognitive bias, use the logically correct framework for interpretation of evidence (the likelihood-ratio framework), and are empirically validated under casework conditions.
Identifiants
pubmed: 35634572
doi: 10.1016/j.fsisyn.2022.100270
pii: S2589-871X(22)00055-9
pmc: PMC9133770
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
100270Informations de copyright
© 2022 The Author.
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