Statistical Challenges in the Quantification of Gunshot Residue Evidence

Romain Gauriot · Lawrence Gunaratnam · Rossana Moroni · Tapani Reinikainen · Jukka Corander

24 citations0/0 files storedJournal of Forensic Sciences

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Legal processes and jurisprudence Statistical Methods and Bayesian Inference Bayesian Modeling and Causal Inference

Abstract

The discharging of a gun results in the formation of extremely small particles known as gunshot residues (GSR). These may be deposited on the skin and clothing of the shooter, on other persons present, and on nearby items or surfaces. Several factors and their complex interactions affect the number of detectable GSR particles, which can deeply influence the conclusions drawn from likelihood ratios or posterior probabilities for prosecution hypotheses of interest. We present Bayesian network models for casework examples and demonstrate that probabilistic quantification of GSR evidence can be very sensitive to the assumptions concerning the model structure, prior probabilities, and the likelihood components. This finding has considerable implications for the use of statistical quantification of GSR evidence in the legal process.

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References

2004Statistics and the Evaluation of Evidence for Forensic Scientists 2010Analysis of Gunshot Residue and Associated Materials—A Review 2001Identification of gunshot residue: a critical review 2009Probabilistic evidential assessment of gunshot residue particle evidence (Part I): Likelihood ratio calculation and case pre-assessment using Bayesian networks 2011Passive Exposure and Persistence of Gunshot Residue (GSR) on Bystanders to a Shooting: Comparison of Shooter and Bystander Exposure to GSR 2006A proposal for statistical evaluation of the detection of gunshot residues on a suspect 2007Equal prior probabilities: Can one do any better?2005A probabilistic approach to the joint evaluation of firearm evidence and gunshot residues

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