Modelling the effects of crime type and evidence on judgments about guilt
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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/17687Published Version (Please cite this version)
10.1038/s41562-018-0451-zPublication 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.
Collections
More Info
Show full item recordScholars@Duke
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
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.
Alphabetical list of authors with Scholars@Duke profiles.

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info