Eyewitness Identification: Debiasing the Effects of Composites and Surveillance Images

Iowa State University, Ames IA

Investigators

Abstract

The advent of forensic DNA testing has helped establish the fact that mistaken eyewitness identification is the largest factor contributing to jury convictions of innocent people. As a result, the system-variable approach to eyewitness identification research, which is designed to reduce mistaken identifications without harming accurate identification rates, has started to have considerable impact in the legal system. In spite of considerable progress in recent years, little is known about how the "paths" through which innocent people become suspects in lineups can bias identifications toward the suspect and how to debias these situations (reduce mistaken identifications) without lowering the chances that the witnesses will identify the actual culprit. Two important "paths" through which innocent people become suspects are the use of composite drawings and the use of surveillance images. Both paths guarantee some propensity for eyewitnesses to identify an innocent suspect if that person became a suspect based in part on the image. It is hypothesized that the dominant recommendation of eyewitness researchers for selecting lineup fillers is not sufficient to debias lineups when either the image-similarity or the composite-similarity paths are operating. This research will test various debiasing techniques. The methodology will use films depicting a terrorist event (12 versions involving 12 different culprits) that will be shown to over 2000 people individually who will then be asked to identify the terrorist from a lineup and state their certainty. Innocent suspects will substitute for the terrorist in half of the lineups. Innocent suspects will be selected based on verbal descriptions, composite drawings, or surveillance images. Debiasing techniques will include methods of selecting lineup fillers, the use of the sequential lineup, and the use of a new technique called the phantom lineup. In addition to traditional analyses, Bayesian information-gain analyses, which assess both the incriminating and exonerating value of a give lineup procedure, will be used to assess the effectiveness of the debiasing procedures. The results of this work will help clarify eyewitness scientists' basic understanding of the processes of false recognition and false certainty. In addition, the results will inform law enforcement about procedures that can be used to create unbiased and informative lineups under conditions in which surveillance images and composite drawings could otherwise bias those results.