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The evidential strength of a combination of corresponding class features in tire examination
Marjan Sjerps1, Ivo Alberink2, Richard Visser2
1Netherlands Forensic Institute, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands; University of Amsterdam, KdVI, PO Box 94248, 1090 GE Amsterdam, the Netherlands.
Abstract:
Tire marks are an important type of forensic evidence as they are frequently encountered at crime scenes. When the tires of a suspect's car are compared, the evidence can be very strong if so-called 'acquired features' are observed to correspond. When only 'class characteristics' such as parts of the tire pattern are observed to correspond, it is obvious that many other tires will exist that also correspond, and so this evidence is usually considered very weak or is simply ignored. Like Benedict et al. (2014) we argue that such evidence can still be strong and should be taken into account. We describe a method for assessing the evidential strength of a set of corresponding class characteristics by presenting a case example from the Netherlands in which tire marks were obtained. Only part of two different tire patterns were visible, in combination with measurements on the axes width. Suitable databases were found already existing and accessible to forensic experts. We show how such data can be used to quantify the strength of evidence and how it can be reported. We also show how the risk of bias due to information surrounding the case may be minimized in cases like this. Our 'blind' procedure enables the expert to report a correspondence between class features in a more convincing way than standard procedures allow. In the particular exemplar case quite strong evidence was obtained, which was accepted and used by the Dutch court. We generalize this procedure for quantifying the evidential value of an expert's opinion of a correspondence. This examination procedure can be applied directly to other types of pattern evidence such as shoeprints, fingerprints, or images. Furthermore, it is 'blind' in the sense that the risk of contextual bias is minimized.
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