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Demonstrative Evidence and the Use of Algorithms in Jury Trials
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 314–332
Rachel Rogers ORCID icon link to view author Rachel Rogers details   Susan VanderPlas ORCID icon link to view author Susan VanderPlas details  

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https://doi.org/10.6339/24-JDS1130
Pub. online: 2 May 2024      Type: Education In Data Science      Open accessOpen Access

Received
31 July 2023
Accepted
6 April 2024
Published
2 May 2024

Abstract

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom – where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.

Supplementary material

 Supplementary Material
The Supplementary Material includes: (1) Statistical models and additional graphs for study questions; (2) Code for the creation of the survey app; (3) Survey data and testimony outline; (4) Source files for paper.

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Copyright
2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Open access article under the CC BY license.

Keywords
explainable machine learning jury perception

Funding
This work was funded (or partially funded) by the Center for Statistics and Applications in Forensic Evidence (CSAFE) through Cooperative Agreements 70NANB15H176 and 70NANB20H019 between NIST and Iowa State University, which includes activities carried out at Carnegie Mellon University, Duke University, University of California Irvine, University of Virginia, West Virginia University, University of Pennsylvania, Swarthmore College and University of Nebraska, Lincoln.

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