Toward a Bayesian Analysis of Recanted Eyewitness Identification Testimony
Kristy L. Fields
The reliability of eyewitness identification has been increasingly questioned in recent years. Despite acknowledgment that such evidence is not only unreliable, but also overly emphasized by judicial decisionmakers, in some cases, antiquated procedural rules and lack of guidance as to how to properly weigh identification evidence produce unsettling results. Troy Anthony Davis was executed in 2011 amidst public controversy regarding the eyewitness evidence against him. At trial, nine witnesses identified Davis as the perpetrator. However, after his conviction, seven of those witnesses recanted. Bogged down by procedural restrictions and long-held judicial mistrust of recantation evidence, Davis never received a new trial and his execution produced worldwide criticism.
On the 250th anniversary of Bayes’ Theorem, this Note applies Bayesian analysis to Davis’s case to demonstrate a potential solution to this uncertainty. By using probability theory and scientific evidence of eyewitness accuracy rates, it demonstrates how a judge might have included the weight of seven recanted identifications to determine the likelihood that the initial conviction was made in error. This Note demonstrates that two identifications and seven nonidentifications results in only a 31.5% likelihood of guilt, versus the 99% likelihood represented by nine identifications. This Note argues that Bayesian analysis can, and should, be used to evaluate such evidence. Use of an objective method of analysis can ameliorate cognitive biases and implicit mistrust of recantation evidence. Furthermore, most arguments against the use of Bayesian analysis in legal settings do not apply to post-conviction hearings evaluating recantation evidence. Therefore, habeas corpus judges faced with recanted eyewitness identifications ought to consider implementing this method.