TY - JOUR
T1 - A strawman with machine learning for a brain
T2 - A response to Biedermann (2022) the strange persistence of (source) “identification” claims in forensic literature
AU - Morrison, Geoffrey Stewart
AU - Ramos, Daniel
AU - Ypma, Rolf JF
AU - Basu, Nabanita
AU - de Bie, Kim
AU - Enzinger, Ewald
AU - Geradts, Zeno
AU - Meuwly, Didier
AU - van der Vloed, David
AU - Vergeer, Peter
AU - Weber, Philip
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Forensic inference
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85130344512&partnerID=8YFLogxK
U2 - 10.1016/j.fsisyn.2022.100230
DO - 10.1016/j.fsisyn.2022.100230
M3 - Editorial
AN - SCOPUS:85130344512
SN - 2589-871X
VL - 4
JO - Forensic Science International: Synergy
JF - Forensic Science International: Synergy
M1 - 100230
ER -