Citation

Cuellar M, Gonzalez C, Dror IE. Forensic Sci. Int. Synergy 2022; 5: e100283.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.fsisyn.2022.100283

PMID

36132433

PMCID

PMC9483780

Abstract

It is unclear whether humans assess similarity differently than automated algorithms in firearms comparisons. Human participants (untrained in firearm examination) were asked to assess the similarity of pairs of images (from 0 to 100). A sample of 40 pairs of cartridge casing 2D-images was used. The images were divided into 4 groups according to their similarity as determined by an algorithm. Humans were able to distinguish between matches and non-matches (both when shown the 2 middle groups, as well as when shown all 4 groups). Thus, humans are able to make high-quality similarity judgments in firearm comparisons based on two images. The humans' similarity scores were superior to the algorithms' scores at distinguishing matches and non-matches, but inferior in assessing similarity within groups. This suggests that humans do not have the same group thresholds as the algorithm, and that a hybrid human-machine approach could provide better identification results than humans or algorithms alone.

Language: en

Keywords

Firearms; Machine learning; Decision-making; Forensic; Error rate; Identification decisions; Similarity