A strawman with machine learning for a brain: A response to Biedermann (2022) the strange persistence of (source) “identification” claims in forensic literature


We agree wholeheartedly with Biedermann (2022) FSI Synergy article 100222 in its criticism of research publications that treat forensic inference in source attribution as an “identification” or “individualization” task. We disagree, however, with its criticism of the use of machine learning for forensic inference. The argument it makes is a strawman argument. There is a growing body of literature on the calculation of well-calibrated likelihood ratios using machine-learning methods and relevant data, and on the validation under casework conditions of such machine-learning-based systems.

Publication DOI: https://doi.org/10.1016/j.fsisyn.2022.100230
Divisions: College of Engineering & Physical Sciences
College of Business and Social Sciences > Aston Institute for Forensic Linguistics
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Additional Information: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license 4.0 Funding Information: The writing of this response was supported by Research England's Expanding Excellence in England Fund as part of funding for the Aston Institute for Forensic Linguistics 2019–2023.
Uncontrolled Keywords: Forensic inference,Machine learning,Pathology and Forensic Medicine,Law
Publication ISSN: 2589-871X
Last Modified: 22 May 2024 07:20
Date Deposited: 06 Jun 2022 11:56
Full Text Link:
Related URLs: https://www.sci ... 0158?via%3Dihub (Publisher URL)
PURE Output Type: Letter
Published Date: 2022-05-19
Published Online Date: 2022-05-06
Accepted Date: 2022-04-25
Authors: Morrison, Geoffrey Stewart (ORCID Profile 0000-0001-8608-8207)
Ramos, Daniel
Ypma, Rolf JF
Basu, Nabanita
Bie, Kim de
Enzinger, Ewald
Geradts, Zeno
Meuwly, Didier
Vloed, David van der
Vergeer, Peter
Weber, Philip (ORCID Profile 0000-0002-3121-9625)



Version: Published Version

License: Creative Commons Attribution

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