University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Evaluating a combination of matching class features: a general 'blind' procedure and a tire marks case

Evaluating a combination of matching class features: a general 'blind' procedure and a tire marks case

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FOSW03 - Statistical Modelling of Scientific Evidence

Co-authors: Ivo Alberink (NFI), Reinoud Stoel (NFI)
 
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 to marks found at a crime scene, the evidence can be very strong if so-called ‘acquired features’ are observed to correspond. When only ‘class characteristics’ such as parts of the profile are observed to correspond, it is obvious that many other tires will exist that correspond equally well. This evidence is, consequently, usually considered very weak or it may simply be ignored. Like Benedict et al. (2014) we argue that such evidence can still be strong and should be taken into account. We explain a method for assessing the evidential strength of a set of matching class characteristics by presenting a case example from the Netherlands in which tire marks were obtained. Only part of two different tire profiles 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 such evidence and how it can be reported. We also show how the risk of contextual bias may be minimized in cases like this. In the particular exemplar case quite strong evidence was obtained, which was accepted and used by the Dutch court. We describe a general procedure for quantifying the evidential value of an expert’s opinion of a ‘match’. This procedure can directly be applied to other types of pattern evidence such as shoeprints, fingerprints, or images. Furthermore, it is ‘blind’ in the sense that context inf ormation management (CIM) is applied to minimize bias.

This talk is part of the Isaac Newton Institute Seminar Series series.

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