Triers of fact sometimes consider lineup fairness when determining the suggestiveness of an identification procedure. Likewise, researchers often consider lineup fairness when comparing results across studies. Despite their importance, lineup fairness measures have received scant empirical attention and researchers inconsistently conduct and report mock-witness tasks and lineup fairness measures. We conducted a large-scale, online experiment (N = 1,010) to examine how lineup fairness measures varied with mock-witness task methodologies as well as to explore the validity and reliability of the measures. In comparison to descriptions compiled from multiple witnesses, when individual descriptions were presented in the mock-witness task, lineup fairness measures indicated a higher number of plausible lineup members but more bias toward the suspect. Target-absent lineups were consistently estimated to be fairer than target-present lineups-which is problematic because it suggests that lineups containing innocent suspects are less likely to be challenged in court than lineups containing guilty suspects. Correlations within lineup size measures and within some lineup bias measures indicated convergent validity and the correlations across the lineup size and lineup bias measures demonstrated discriminant validity. The reliability of lineup fairness measures across different descriptions was low and reliability across different sets of mock witnesses was moderate to high, depending on the measure. Researchers reporting lineup fairness measures should specify the type of description presented, the amount of detail in the description, and whether the mock witnesses viewed target-present and/or -absent lineups. (PsycINFO Database Record
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Sci Rep
May 2024
Department of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Recent advances in artificial intelligence (AI) enable the generation of realistic facial images that can be used in police lineups. The use of AI image generation offers pragmatic advantages in that it allows practitioners to generate filler images directly from the description of the culprit using text-to-image generation, avoids the violation of identity rights of natural persons who are not suspects and eliminates the constraints of being bound to a database with a limited set of photographs. However, the risk exists that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish AI-generated filler images from the photograph of the suspect's face.
View Article and Find Full Text PDFFront Psychol
April 2024
Department of Psychology, The Graduate Center, City University of New York, New York, NY, United States.
Introduction: Despite converging evidence that people more closely associate the construct of criminality with Black people who exhibit a more African facial phenotype than Black people who express a more European phenotype, eyewitness researchers have largely ignored phenotypic bias as a potential contributor to the racial disparities in the criminal legal system. If this form of phenotypic bias extends to eyewitness identification tasks, eyewitnesses may be more likely to identify Black suspects with an African rather than European phenotype, regardless of their guilt status. Further, in cases where the witness's description of the perpetrator does not contain phenotypic information, phenotypic mismatch between the suspect and the other lineup members may bias identification decisions toward or against the suspect.
View Article and Find Full Text PDFPLoS One
December 2023
Department of Psychology, Friedrich Schiller University Jena, Jena, Germany.
Empirical investigations into eyewitness identification accuracy typically necessitate the creation of novel stimulus materials, which can be a challenging and time-consuming task. To facilitate this process and promote further research in this domain, we introduce the new Jena Eyewitness Research Stimuli (JERS). They comprise six video sequences depicting a mock theft committed by two different perpetrators, available in both two-dimensional (2D) and 360° format, combined with the corresponding lineup images presented in 2D or three-dimensional (3D) format.
View Article and Find Full Text PDFSci Rep
April 2023
Department of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
The mock-witness task is typically used to evaluate the fairness of lineups. However, the validity of this task has been questioned because there are substantial differences between the tasks for mock witnesses and eyewitnesses. Unlike eyewitnesses, mock witnesses must select a person from the lineup and are alerted to the fact that one lineup member might stand out from the others.
View Article and Find Full Text PDFSci Rep
September 2022
Department of Experimental Psychology, Heinrich Heine University Düsseldorf, 40204, Düsseldorf, Germany.
To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups.
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