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 J.F. Ypma, Nabanita Basu, Kim de Bie, Ewald Enzinger, Zeno Geradts, Didier Meuwly, David van der Vloed, Peter Vergeer, Philip Weber · Forensic Science International Synergy · 2022
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.