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

Abstract

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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Fig. 1: Task design and manipulation checks.
Fig. 2: Evidence and crime effects on subject ratings for confidence in guilt.
Fig. 3: Similarities and differences between potential jurors and legally trained participants.
Fig. 4: Crime effects on confidence in guilt are positively correlated with seriousness of the crime.

Data availability

All data used in the analyses are available and documented at https://github.com/pearsonlab/legal.

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Acknowledgements

We thank D. Angel for valuable discussion throughout this project. A. Demasi, M. Rabil, M. Nerheim, P. Campos and A. Buras provided invaluable help recruiting subjects for these studies. We are especially grateful to the law students, prosecutors and other lawyers who volunteered their time as research subjects. This work was supported by an Incubator Award from the Duke Institute for Brain Sciences, NSF grant no. 1655445 and by a career development award from the NIH Big Data to Knowledge Program (grant no. K01-ES-025442 to J.M.P.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Contributions

J.M.P. and J.H.P.S. conceived the project. All authors contributed to the task design. D.A.B., D.H.B. and J.A.G.S. wrote the crime scenarios and evidence modules. J.L., J.A.G.S. and J.M.P. designed and coded the online task, ran the mTurk experiments and processed the data. J.H.P.S. and D.H.B. recruited the legally trained subjects and administered the in-person sessions. J.M.P. wrote/designed the computational models. J.L., J.A.G.S. and J.M.P. analysed the data. J.H.P.S., J.M.P. and R.M.C. wrote the paper.

Corresponding author

Correspondence to J. H. Pate Skene.

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Competing Interests

D.A.B. and A.M. are partners in a trial consulting firm, whose practise includes civil litigation and criminal defence. A.M., J.H.P.S. and D.H.B. are attorneys, part of whose practises include criminal defence. J.H.P.S. works with both criminal defence attorneys and prosecutors. D.H.B. represents plaintiffs in civil cases and death row inmates in post-conviction criminal cases.

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Supplementary Methods, Supplementary Tables 1–10, Supplementary Figures 1–12, Supplementary References

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Pearson, J.M., Law, J.R., Skene, J.A.G. et al. Modelling the effects of crime type and evidence on judgments about guilt. Nat Hum Behav 2, 856–866 (2018). https://doi.org/10.1038/s41562-018-0451-z

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