Human and machine similarity judgments in forensic firearm comparisons

Maria Cuellar; Cleotilde Gonzalez; Itiel E Dror

doi:10.1016/j.fsisyn.2022.100283

Human and machine similarity judgments in forensic firearm comparisons

Forensic Sci Int Synerg. 2022 Aug 23:5:100283. doi: 10.1016/j.fsisyn.2022.100283. eCollection 2022.

Authors

Maria Cuellar¹, Cleotilde Gonzalez², Itiel E Dror³

Affiliations

¹ University of Pennsylvania, Department of Criminology and Department of Statistics and Data Science, 3718 Locust Walk, Philadelphia, PA, 19104, USA.
² Carnegie Mellon University, Department of Social and Decision Sciences, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
³ University College London, 35 Tavistock Square, London, WC1H 9EZ, UK.

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.

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