Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

Geoffrey Stewart Morrison

doi:10.1016/j.fsisyn.2022.100270

Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

Research output: Contribution to journal › Review article › peer-review

Abstract

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.

Original language	English
Article number	100270
Journal	Forensic Science International: Synergy
Volume	5
Early online date	18 May 2022
DOIs	https://doi.org/10.1016/j.fsisyn.2022.100270
Publication status	Published - May 2022

Bibliographical note

© 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license 4.0

Funding: This research was supported by Research England's Expanding Excellence in England Fund as part of funding for the Aston Institute for Forensic Linguistics 2019–2023.

Keywords

Forensic data science
Forensic science
Likelihood ratio
Paradigm shift
Validation

Access to Document

10.1016/j.fsisyn.2022.100270Licence: CC BY 4.0

Advancing a paradigm shift in evaluation of forensic evidence
© 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license 4.0
Final published version, 492 KBLicence: CC BY 4.0

Cite this

@article{8b7bbd854111441581d5240b31a403e5,

title = "Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science",

abstract = "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.",

keywords = "Forensic data science, Forensic science, Likelihood ratio, Paradigm shift, Validation",

author = "Morrison, {Geoffrey Stewart}",

note = "{\textcopyright} 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license 4.0 Funding: This research was supported by Research England's Expanding Excellence in England Fund as part of funding for the Aston Institute for Forensic Linguistics 2019–2023.",

year = "2022",

month = may,

doi = "10.1016/j.fsisyn.2022.100270",

language = "English",

volume = "5",

}

TY - JOUR

T1 - Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

AU - Morrison, Geoffrey Stewart

N1 - © 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license 4.0 Funding: This research was supported by Research England's Expanding Excellence in England Fund as part of funding for the Aston Institute for Forensic Linguistics 2019–2023.

PY - 2022/5

Y1 - 2022/5

N2 - 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.

AB - 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.

KW - Forensic data science

KW - Forensic science

KW - Likelihood ratio

KW - Paradigm shift

KW - Validation

UR - https://www.sciencedirect.com/science/article/pii/S2589871X22000559?via%3Dihub

UR - http://www.scopus.com/inward/record.url?scp=85130599644&partnerID=8YFLogxK

U2 - 10.1016/j.fsisyn.2022.100270

DO - 10.1016/j.fsisyn.2022.100270

M3 - Review article

C2 - 35634572

SN - 2589-871X

VL - 5

JO - Forensic Science International: Synergy

JF - Forensic Science International: Synergy

M1 - 100270

ER -

Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this