dc.contributor.author Fenton, NE en_US dc.contributor.author Neil, M en_US dc.contributor.author Hsu, A en_US dc.date.accessioned 2015-04-14T15:03:49Z dc.date.issued 2014 en_US dc.identifier.issn 0924-8463 en_US dc.identifier.other 10.1007/s10506-013-9147-x dc.identifier.uri http://link.springer.com/article/10.1007%2Fs10506-013-9147-x# dc.identifier.uri http://qmro.qmul.ac.uk/xmlui/handle/123456789/7249 dc.description.abstract It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases of such features, a serious challenge to the use of Bayes in such legal contexts is that its standard formulaic representations are not readily understandable to non-statisticians. Attempts to get round this problem usually involve representations based around some variation of an event tree. While this approach works well in explaining the most trivial instance of Bayes’ theorem (involving a single hypothesis and a single piece of evidence) it does not scale up to realistic situations. In particular, even with a single piece of match evidence, if we wish to incorporate the possibility that there are potential errors (both false positives and false negatives) introduced at any stage in the investigative process, matters become very complex. As a result we have observed expert witnesses (in different areas of speciality) routinely ignore the possibility of errors when presenting their evidence. To counter this, we produce what we believe is the first full probabilistic solution of the simple case of generic match evidence incorporating both classes of testing errors. Unfortunately, the resultant event tree solution is too complex for intuitive comprehension. And, crucially, the event tree also fails to represent the causal information that underpins the argument. In contrast, we also present a simple-to-construct graphical Bayesian Network (BN) solution that automatically performs the calculations and may also be intuitively simpler to understand. Although there have been multiple previous applications of BNs for analysing forensic evidence—including very detailed models for the DNA matching problem, these models have not widely penetrated the expert witness community. Nor have they addressed the basic generic match problem incorporating the two types of testing error. Hence we believe our basic BN solution provides an important mechanism for convincing experts—and eventually the legal community—that it is possible to rigorously analyse and communicate the full impact of match evidence on a case, in the presence of possible errors en_US dc.format.extent 1 - 28 (29) en_US dc.language English en_US dc.language.iso en en_US dc.publisher Springer LInk en_US dc.relation.ispartof Artificial Intelligence and Law en_US dc.subject Bayes en_US dc.subject Likelihood ratio en_US dc.subject Forensic match en_US dc.subject Evidence en_US dc.title Calculating and understanding the value of any type of match evidence when there are potential testing errors en_US dc.type Article dc.identifier.doi 10.1007/s10506-013-9147-x en_US pubs.issue 1 en_US pubs.notes No embargo en_US pubs.publication-status Published en_US pubs.publisher-url http://link.springer.com/article/10.1007/s10506-013-9147-x en_US pubs.volume 22 en_US
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