Upper Limits of Eyewitness Identification Accuracy in Court

Eyewitnesses in real life make erroneous identifications, as shown by compilations of a century’s worth of newspaper accounts of erroneous indictments and/ or convictions based on mistaken eyewitnesses, and based on published descriptions of exonerations since 1989 that show that the majority of the erroneous convictions resulted from erroneous eyewitness identifications. These accounts do not indicate an error rate, only a high number of instances. To estimate error rates with which eyewitnesses identify innocent suspects in police lineups and innocent defendants in court, we review and evaluate five independent lines of research. These include: (1) laboratory experimental research on face recognition showing the accuracy of recognizing unfamiliar faces seen just once before, in the absence of a crime; (2) laboratory experimental research from 1970 to the present in which subjects observe a crime and attempt an identification from a lineup; (3) field research studies from the showing the accuracy of the identification of a “perpetrator,” in the absence of a crime; (4) military laboratory research data showing the accuracy with which soldiers can identify their interrogators (perpetrators) from a lineup 24 hours after intense and stressful questioning; and (5) analyses of police archival data showing the percentage of time that an eyewitness picks the person the police have placed in the lineup as the suspect. These data bases, taken together, establish an upper limit on eyewitness identification accuracy of less than 50% correct (correct identification rate and/or correct rejection of the lineup when the perpetrator is not in the lineup). They further indicate that most real eyewitnesses to real crimes are unable to achieve even this modest level of accuracy when they testify in court to an identification of a defendant as the perpetrator. 1 Ralph Norman Haber and Lyn Haber Eyewitness Accuracy Upper Limits on Eyewitness Identification Accuracy in Court The purpose of this article is to review the available evidence on the accuracy with which an eyewitness identifies the perpetrator of a crime from a lineup, and, based on that evidence, project the accuracy levels of real eyewitnesses when they testify in court to an identification of the defendant as the perpetrator. The photospread lineup is the principal technique used by police officials to elicit an identification of a suspect as the perpetrator (Behrman & Davey, 2001). Live lineups are rare, but current evidence suggests that identification accuracy is equivalent to photo-spreads, whereas live showups are more frequently used, but current evidence suggests lower accuracy levels than found with multi-person photospread and live lineups (Stablay et al., 2003). Eyewitness identification accuracy levels, until quite recently, have not been directly investigated. Up to the past decade, the main source of data has been experimental laboratory research, using college students as subjects. The purpose of those studies has been almost exclusively to investigate different factors that affect the accuracy of identifications, not the absolute levels of accuracy. Even when reported, which is not always done, the absolute accuracy levels found in those studies have not used to estimate the accuracy with which real eyewitnesses can pick the suspect out of a real lineup administered by real police. The purpose of this article is to estimate the accuracy rate with which an eyewitness to a crime is likely correctly to identify the defendant as the perpetrator of that crime. Since we already know that many factors and variables influence accuracy levels, our focus in this article is to estimate the upper limit that can be expected on identification accuracy under the factors known to be present. We believe that the public, the legal system, and law enforcement agencies have all assumed that the upper limit on identification accuracy, under optimal circumstances of observation, human memory, and police investigation procedures, is close to or at 100% correct. We will show in this article that this optimality assumption is grossly incorrect, due to normal limitations in human perception and memory, especially of strangers, and for additional limitations produced by fear and distress. Our goal is to provide estimates on what this upper limit might be, for the various conditions affecting the eyewitness in producing an identification of a perpetrator of a crime. These upper limit estimates are extracted from the research to be reviewed here such that they can be applied to the probability that the testimony of an eyewitness to the identification of the defendant as the perpetrator in court may be correct or erroneous. We begin by reviewing a century’s worth of accounts showing that erroneous eyewitness identifications have been used to indict and convict innocent suspects, and published exonerations from 1989 to the present based on DNA and other postconviction evidence which show the majority of these convictions rested on erroneous eyewitness identifications. Then we examine five independent and very different research data bases which we use to estimate upper limits for the accuracy rates of eyewitness identification. The first provides estimates of the accuracy with which subjects in experimental research can recognize faces seen only once in non-crime settings. We consider this data base first, because it establishes a pure measure of the great difficulty of the identification task when the person to be recognized is a stranger, seen but once before. It is pure because identification accuracy is measured in the absence of the multitude of 2 Ralph Norman Haber and Lyn Haber Eyewitness Accuracy variables that further impede the accuracy of the eyewitness who observes a crime. The results of these studies provide an upper limit on the accuracy with which a witness can identify someone who is a stranger under otherwise optimal conditions. The second data base contains the large corpus of laboratory accuracy rate research on college student eyewitnesses who observe a crime and identify the suspect in a lineup. These results, unlike from the first data base, include the basic ingredients of eyewitness identifications: the witness observes a crime, a person suspected of that crime is shown in a lineup; and the identification results allow examination of the factors that affect accuracy, including the methods used to elicit identifications. But these studies are missing the presence of the normally high levels of fear, terror and stress present in witnesses to most crimes: these laboratory ‘crimes’ are of necessity gentle. They also use almost exclusively a subject population that is not typical of most real live eyewitnesses. These accuracy results represent a different upper limit on eyewitness identification accuracy, because the subject/eyewitness observes and makes the identification under more idealized conditions than a real witness to a real crime. The third data base contains accuracy rates from non-crime field lineup identification studies in which eyewitnesses identify persons they observed under focused attention. These experiments do not involve crimes, so there is no fear or stress, and they are designed to insure the witness’s focused attention, but they do correct two of the problems with the laboratory research: the subjects in the field studies are more representative of those who witness crimes; and, more importantly, the eyewitnesses do not know they are being tested or observed. This data base provides another upper limit on eyewitness identification accuracy of a stranger, but under focused attention and in the absence of fear and stress. The fourth data base includes recent military research in which soldiers are being trained to resist interrogation. Each soldier is ‘captured’ and interrogated under intense conditions. After the interrogation, he is asked to identify his interrogator from a live lineup. The results from this research permit estimates of accuracy when the witness experiences great fear comparable to what eyewitnesses to violent crimes commonly feel. The two limitations are the atypical soldier population, and their lengthy, focused, well-lit view of their interrogators. The last data base includes actual police records of real crimes in which real eyewitnesses make identifications from lineups constructed by the police that contain the suspect the police believe is the perpetrator. The data reported are the actual rates with which eyewitnesses pick the suspect from the lineup. These suspect identification rates are exactly the estimate sought: real eyewitnesses of real crimes identifying real suspects from real lineups administered by real police. However, since the police’s suspect may not always be the true perpetrator, the identification accuracy rate again provides only an upper limit, one dependent on the accuracy of the police in finding the suspect who is the true perpetrator. Taken singly, none of these data sources can be used to estimate the accuracy of eyewitness identifications in real lineups. We review the problematic nature of each individual data base in some detail. Despite the limitations of each one considered separately, these five data sets can be viewed together to set upper limits on accuracy in real life circumstances. We will show that these limits converge toward a single value that can be applied to most eyewitness identifications based on observation of a real 3 Ralph Norman Haber and Lyn Haber Eyewitness Accuracy crime. The enormous advantage of estimates based on experimental work (the first four data bases above) is that the ground truth of the identification is known: every identification response made by the eyewitness can be scored as correct or incorrect. In the police archival data, in which the true perpetrator is unknown, true identification accuracy rate cannot be known and the results provide only an