ArticleScientific reports2024
On the advantages of using AI-generated images of filler faces for creating fair lineups.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it.British journal of psychology (London, England : 1953) · 2026Article
- Validating a multinomial processing tree model for measuring confidence in lineups using a post-response feedback manipulation.Cognitive research: principles and implications · 2026Article
- Early Emergence of Spontaneous Trait Content in Children's Unconstrained Impressions of Faces.Developmental science · 2026Article
- Towards synthetic fillers for fair photo lineups: application of generative AI in criminal law proceedings.Frontiers in artificial intelligence · 2026Article
- Detection of culprit presence in multiple-culprit crimes: A comparison of combined and separate lineup-presentation formats.PloS one · 2026Article
- Delays reduce culprit-presence detection but do not affect guessing-based selection in response to lineups.Scientific reports · 2025Article
- The effect of lineup size on discriminability is dependent on filler similarity and independent of encoding strength.Memory & cognition · 2025Article
- Lineup position affects guessing-based selection but not culprit-presence detection in simultaneous and sequential lineups.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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. Using a model-based analysis, we compared biased suspect selection directly between lineups with AI-generated filler images and lineups with database-derived filler photographs. The results show that the lineups with AI-generated filler images were perfectly fair and, in fact, led to less biased suspect selection than the lineups with database-derived filler photographs used in previous experiments. These results are encouraging with regard to the potential of AI image generation for constructing fair lineups which should inspire more systematic research on the feasibility of adopting AI technology in forensic settings.
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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.