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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 fore...

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Published in:Forensic science international. Synergy 2022-01, Vol.4, p.100230-100230, Article 100230
Main Authors: Morrison, Geoffrey Stewart, Ramos, Daniel, Ypma, Rolf JF, Basu, Nabanita, de Bie, Kim, Enzinger, Ewald, Geradts, Zeno, Meuwly, Didier, van der Vloed, David, Vergeer, Peter, Weber, Philip
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Language:English
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Summary: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.
ISSN:2589-871X
2589-871X
DOI:10.1016/j.fsisyn.2022.100230