Artificial intelligence as a tool for detecting deception in 911 homicide calls

Patrick M. Markey, Samantha Goldman, Jennie Dapice, Sofia Saj, Saadet Ceynek, Tia Nicolas and Lila Trollip

Journal of Criminal Justice, 2025, vol. 96, issue C

Abstract: This paper investigates the application of Artificial Intelligence (AI), specifically the use of a Large Language Model (ChatGPT), in analyzing 911 calls to identify deceptive reports of homicides. The study sampled an equal number of False Allegation Callers (FACs) and True Report Callers (TRCs), categorized through judicial outcomes. Calls were processed using ChatGPT, which assessed 86 behavioral cues from 142 callers. Using a random forest model with k-fold cross-validation and repeated sampling, the analysis achieved an accuracy rate of 70.68 %, with sensitivity and specificity rates at 71.44 % and 69.92 %, respectively. The study revealed distinct behavioral patterns that differentiate FACs and TRCs. AI characterized FACs as somewhat unhelpful and emotional, displaying behaviors such as awkwardness, unintelligibility, moodiness, uncertainty, making situations more complicated, expressing regret, and self-dramatizing. In contrast, AI identified TRCs as helpful and composed, marked by responsiveness, cooperativeness, a focus on relevant issues, consistency, plausibility in their messages, and candidness.

Keywords: Violent crime; Large language models; Deception; 911 calls; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jcjust:v:96:y:2025:i:c:s0047235224001867

DOI: 10.1016/j.jcrimjus.2024.102337

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