ArticleCognitive research: principles and implications2024
Application of artificial intelligence to eyewitness identification.
Article in Cognitive research: principles and implications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 6 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is already all around us, and its usage will only increase. Knowing its capabilities is critical. A facial recognition system (FRS) is a tool for law enforcement during suspect searches and when presenting photos to eyewitnesses for identification. However, there are no comparisons between eyewitness and FRS accuracy using video, so it is unknown whether FRS face matches are more accurate than eyewitness memory when identifying a perpetrator. Ours is the first application of artificial intelligence to an eyewitness experience, using a comparative psychology approach. As a first step to test system accuracy relative to eyewitness accuracy, participants and an open-source FRS (FaceNet) attempted perpetrator identification/match from lineup photos (target-present, target-absent) after exposure to real crime videos with varied clarity and perpetrator race. FRS used video probe images of each perpetrator to achieve similarity ratings for each corresponding lineup member. Using receiver operating characteristic analysis to measure discriminability, FRS performance was superior to eyewitness performance, regardless of video clarity or perpetrator race. Video clarity impacted participant performance, with the unclear videos yielding lower performance than the clear videos. Using confidence-accuracy characteristic analysis to measure reliability (i.e., the likelihood the identified suspect is the actual perpetrator), when the FRS identified faces with the highest similarity values, they were accurate. The results suggest FaceNet, or similarly performing systems, may supplement eyewitness memory for suspect searches and subsequent lineup construction and knowing the system's strengths and weaknesses is critical.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.