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A Graphical Model Approach to Eyewitness Identification Data

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FOSW02 - Bayesian Networks and Argumentation in Evidence Analysis

Although eyewitness identification is generally regarded as relatively inaccurate among cognitive psychologists and other experts, testimony from eyewitnesses continues to be prolific in the court system today. There is great interest among psychologists and the criminal justice system to reform eyewitness identification procedures to make the outcomes as accurate as possible. There has been a recent push to adopt Receiver Operating Characteristic (ROC) curve methodology to analyze lineup procedures, but has not been universally accepted in the field. This work addresses some of the shortcomings of the ROC approach and proposes an analytical approach based on log-linear models as an alternative method to evaluate lineup procedures. Unlike approaches that emphasize correct and incorrect identifications and rejections, our log-linear model approach can distinguish among all possible outcomes and allows for a more complete understanding of the variables at work during a lineup task. Due to the high-dimensional nature of the resulting model, representing the results through a dependence graph leads to a deeper understanding of conditional dependencies and causal relationships between variables involved. We believe that graphical models have been under-utilized in the field, and demonstrate their utility for not only broader statistical insights, but as an intuitive way to communicate complex relationships between variables to practitioners. We find that log-linear models can incorporate more information than previous approaches, and provide flexibility needed for data of this nature

This talk is part of the Isaac Newton Institute Seminar Series series.

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