Analytic Hierarchy Process (AHP) and Eyewitness Identification

With my colleague Enrique Mu, I have been working on an innovative research study where they apply the popular operations research tool Analytic Hierarchy Process (AHP), conventionally used for managerial decision making, to the process of eyewitness identification with police line-ups.

According to the Innocence Project, eyewitness misidentification is the number one cause of wrongful convictions. National Institute of Justice published a guide to eyewitness evidence (Technical Working Group for Eyewitness Evidence, 1999) under then Attorney General Janet Reno’s directive that law enforcement present suspect photos one at a time (i.e., the sequential method), rather than simultaneously, as practiced by the law enforcement for decades. However, the sequential method only improves identification accuracy modestly, and only in certain situations.

We simulated police line-up presentation with two suspects at a time in an experiment conducted with over 100 Carlow students. The pairwise presentation method, based on AHP principles, not only presents a new opportunity for improving eyewitness identification accuracy, it also produces many performance metrics that are unavailable with conventional methods. These metrics allow law enforcement to quantify the reliability of the eyewitness in a more objective manner. Preliminary research findings are encouraging, although a larger sample size is needed to achieve a more robust effect size.

We presented preliminary findings from this study at Teachers College, Columbia University in 2012, andwill present the completed study at two international conferences in summer 2013: The 12th International Symposium of the Analytic Hierarchy Process/Analytic Network Process (ISAHP) in Kuala Lumpur, Malaysia, and The Society for Applied Research in Memory and Cognition X (SARMAC X) in Rotterdam, The Netherlands.

This research was funded by a seed grant from the Grace Ann Geibel Institute for Justice and Social Responsibility in 2012 and is currently funded as a signature project of the Geibel Institute in 2013. We would like to thank Carlow alum Jennifer Bourne for her diligent assistance with data collection, entry, and processing.


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