AI assistance improves people’s ability to distinguish correct from incorrect eyewitness lineup identifications - Lauren E. Kelso; David G. Dobolyi; Jesse H. Grabman; Chad S. Dodson (Proceedings of the National Academy of Sciences, 2025)
Mistaken eyewitness identification is one of the leading causes of false convictions. Improving law enforcement’s ability to identify correct identifications could have profound implications for criminal justice. Across two experiments, we show that AI-assistance can improve people’s ability to distinguish between accurate and inaccurate eyewitness lineup identifications. Participants (Experiment 1: N = 1,092, Experiment 2: N = 1,809) saw an eyewitness’s lineup identification, accompanied by the eyewitness’s verbal confidence statement (e.g., “I’m pretty sure”) and either a featural (“I remember his eyes”), recognition (“I remember him”), or familiarity (“He looks familiar”) justification. They then judged the accuracy of the eyewitness’s identification. AI-assistance (vs. no assistance) improved people’s ability to distinguish between correct identifications and misidentifications, but only when they evaluated lineup identifications based on recognition or featural justifications. Discrimination of identifications based on familiarity justifications showed little improvement with AI-assistance. This project is a critical step in evaluating human-algorithm interactions before widespread use of AI-assistance by law enforcement.