Demonstrative Evidence and the Use of Algorithms in Jury Trials

TL;DR

This study examines how demonstrative evidence and algorithms influence juror perceptions, revealing high reliability ratings regardless of evidence type, and discusses implications for forensic science and courtroom evaluations.

Contribution

It provides new insights into juror perceptions of forensic evidence and highlights the phenomenon of scale compression affecting response variability.

Findings

Jurors rated evidence highly on reliability and credibility.

Scale compression limited statistical analysis.

Potential for new experimental designs in forensic perception studies.

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.