Literature DB >> 30802644

Beliefs about error rates and human judgment in forensic science.

Gianni Ribeiro1, Jason M Tangen2, Blake M McKimmie3.   

Abstract

Forensic science techniques are often used in criminal trials to infer the identity of the perpetrator of crime and jurors often find this evidence very persuasive. Unfortunately, two of the leading causes of wrongful convictions are forensic science testing errors and false or misleading forensic testimony (Saks and Koehler, 2005). Therefore, it is important to understand jurors' pre-existing beliefs about forensic science, as these beliefs may impact how they evaluate forensic evidence in the courtroom. In this study, we examine people's perceptions of the likelihood of error and human judgment involved at each stage of the forensic science process (i.e., collection, storage, testing, analysis, reporting, and presenting). In addition, we examine people's perceptions of the accuracy of - and human judgment involved in - 16 different forensic techniques. We find that, in contrast to what would be expected by the CSI effect literature, participants believed that the process of forensic science involved considerable human judgment and was relatively error-prone. In addition, participants had wide-ranging beliefs about the accuracy of various forensic techniques, ranging from 65.18% (document analysis) up to 89.95% (DNA). For some forensic techniques, estimates were lower than that found in experimental proficiency studies, suggesting that our participants are more skeptical of certain forensic evidence than they need to be.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Accuracy; CSI effect; Error rate; Forensic evidence; Forensic science

Mesh:

Year:  2019        PMID: 30802644     DOI: 10.1016/j.forsciint.2019.01.034

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  2 in total

Review 1.  Interpol review of fingermarks and other body impressions 2016-2019.

Authors:  Andy Bécue; Heidi Eldridge; Christophe Champod
Journal:  Forensic Sci Int       Date:  2020-03-17       Impact factor: 2.395

2.  Deep Learning-Based Intelligent Robot in Sentencing.

Authors:  Xuan Chen
Journal:  Front Psychol       Date:  2022-07-18
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.