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

IF 3.3 1区社会学 Q1 CRIMINOLOGY & PENOLOGY

Journal of Criminal Justice Pub Date : 2025-01-01 DOI:10.1016/j.jcrimjus.2024.102337

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

{"title":"Artificial intelligence as a tool for detecting deception in 911 homicide calls","authors":"Patrick M. Markey, Samantha Goldman, Jennie Dapice, Sofia Saj, Saadet Ceynek, Tia Nicolas, Lila Trollip","doi":"10.1016/j.jcrimjus.2024.102337","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48272,"journal":{"name":"Journal of Criminal Justice","volume":"96 ","pages":"Article 102337"},"PeriodicalIF":3.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Criminal Justice","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0047235224001867","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CRIMINOLOGY & PENOLOGY","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Criminal Justice CRIMINOLOGY & PENOLOGY-

CiteScore

6.90

自引率

9.10%

发文量

审稿时长

23 days

期刊介绍： The Journal of Criminal Justice is an international journal intended to fill the present need for the dissemination of new information, ideas and methods, to both practitioners and academicians in the criminal justice area. The Journal is concerned with all aspects of the criminal justice system in terms of their relationships to each other. Although materials are presented relating to crime and the individual elements of the criminal justice system, the emphasis of the Journal is to tie together the functioning of these elements and to illustrate the effects of their interactions. Articles that reflect the application of new disciplines or analytical methodologies to the problems of criminal justice are of special interest. Since the purpose of the Journal is to provide a forum for the dissemination of new ideas, new information, and the application of new methods to the problems and functions of the criminal justice system, the Journal emphasizes innovation and creative thought of the highest quality.