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Modelling the effects of crime type and evidence on judgments about guilt

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Date
2018-11-01
Authors
Beskind, D
Pearson, J
Law, J
Skene, J
Vidmar, N
Ball, D
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Abstract
© 2018, The Author(s), under exclusive licence to Springer Nature Limited. Concerns over wrongful convictions have spurred an increased focus on understanding criminal justice decision-making. This study describes an experimental approach that complements conventional mock-juror experiments and case studies by providing a rapid, high-throughput screen for identifying preconceptions and biases that can influence how jurors and lawyers evaluate evidence in criminal cases. The approach combines an experimental decision task derived from marketing research with statistical modelling to explore how subjects evaluate the strength of the case against a defendant. The results show that, in the absence of explicit information about potential error rates or objective reliability, subjects tend to overweight widely used types of forensic evidence, but give much less weight than expected to a defendant’s criminal history. Notably, for mock jurors, the type of crime also biases their confidence in guilt independent of the evidence. This bias is positively correlated with the seriousness of the crime. For practising prosecutors and other lawyers, the crime-type bias is much smaller, yet still correlates with the seriousness of the crime.
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Journal article
Permalink
https://hdl.handle.net/10161/17687
Published Version (Please cite this version)
10.1038/s41562-018-0451-z
Publication Info
Beskind, D; Pearson, J; Law, J; Skene, J; Vidmar, N; & Ball, D (2018). Modelling the effects of crime type and evidence on judgments about guilt. Nature Human Behaviour, 2(11). pp. 856-866. 10.1038/s41562-018-0451-z. Retrieved from https://hdl.handle.net/10161/17687.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Beskind

Donald H. Beskind

Professor of the Practice of Law
Donald H. Beskind directs and teaches in Duke Law School’s Trial Practice program and teaches Torts and Evidence.  He has been a trial lawyer that represented plaintiffs in civil cases and defendants in criminal cases throughout his career.  After beginning his career in practice in Denver, he was a John S. Bradway Fellow at Duke Law from 1975 to 1977, at the conclusion of which he received his LLM. He then joined the governing faculty, first as an assistant professor
Pearson

John Pearson

Assistant Professor of Neurobiology
My research focuses on the application of machine learning methods to the analysis of brain data and behavior. I have a special interest in the neurobiology of reward and decision-making, particularly issues surrounding foraging, impulsivity, and self-control. More generally, I am interested in computational principles underlying brain organization at the mesoscale, and work in my lab studies phenomena that range from complex social behaviors to coding principles of the retina.
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