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

Geoffrey Stewart Morrison*, Daniel Ramos, Rolf JF Ypma, Nabanita Basu, Kim de Bie, Ewald Enzinger, Zeno Geradts, Didier Meuwly, David van der Vloed, Peter Vergeer, Philip Weber

*Corresponding author for this work

Research output: Contribution to journalLetter, comment or opinionpeer-review

Abstract

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.
Original languageEnglish
Article number100230
Number of pages2
JournalForensic Science International: Synergy
Volume4
Early online date6 May 2022
DOIs
Publication statusPublished - 19 May 2022

Bibliographical note

© 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.

Keywords

  • Forensic inference
  • Machine learning

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