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.

Objectifying evidence evaluation for gunshot residue comparisons using machine learning on criminal case data.

Comparative gunshot residue analysis addresses relevant forensic questions such as 'did suspect X fire shot Y?'. More formally, it weighs the evidence for hypotheses of the form H: gunshot residue particles found on suspect's hands are from the same source as the gunshot residue particles found on the crime scene and H: two sets of particles are from different sources. Currently, experts perform this analysis by evaluating the elemental composition of the particles using their knowledge and experience.