Using Brain Imaging for Lie Detection: Where Science, Law, and Policy Collide
Daniel D. Langleben
University of Pennsylvania - School of Medicine
Jane Campbell Moriarty
Duquesne University - School of Law
March 1, 2012
Psychology, Public Policy, and Law, September 2012
Duquesne University School of Law Research Paper No. 2012-12
Progress in the use of functional magnetic resonance imaging (fMRI) of the brain to differentiate lying from truth-telling has created an expectation of a breakthrough in the search for objective methods of lie detection. In the last few years, litigants have attempted to introduce fMRI-based lie detection evidence in courts. Both the science and its possible use as courtroom evidence have spawned much scholarly discussion. This article contributes to the interdisciplinary debate by identifying the missing pieces of the scientific puzzle that need to be completed if fMRI-based lie detection is to meet the standards of either legal reliability or general acceptance. The article provides a balanced analysis of the current science and the cases in which litigants have sought to introduce fMRI-based lie detection. Identifying the key limitations of the science as expert evidence, the article explores the problems that arise from using scientific evidence before it is proven valid and reliable. We conclude that the Daubert’s “known error rate” is the key concept linking the legal and scientific standards. We suggest that properly controlled clinical trials are the most convincing means to confirm or disprove the relevance of this promising laboratory research. Given the controversial nature and potential societal impact of this technology, collaboration of several government agencies may be required to sponsor impartial and comprehensive clinical trials that will guide the development of forensic fMRI technology.
Number of Pages in PDF File: 14
Keywords: Functional magnetic resonance imaging, fMRI, brain, deception, lie detection, neuroscience, evidence, neuroimaging, scientific evidence, lyingAccepted Paper Series
Date posted: September 1, 2012 ; Last revised: February 24, 2014
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