Naomi Kaplan-Damary, W. Thompson, R. Grady, H. Stern
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Using mixture models to examine group difference among jurors: an illustration involving the perceived strength of forensic science evidence
The way in which jurors perceive reports of forensic evidence is of critical importance, especially in cases of forensic identification evidence that require examiners to compare items and assess whether they originate from a common source. The current study discusses methods for studying group differences among mock jurors and illustrates them using a reanalysis of data regarding lay perceptions of forensic science evidence. Conventional approaches that consider subpopulations defined a priori are compared with mixture models that infer group structure from the data, allowing detection of subgroups that cohere in unexpected ways. Mixture models allow researchers to determine whether a population comprises subpopulations that respond to evidence differently and then to consider how those subpopulations might be characterized. The reanalysis reported here shows that mixture models can enhance understanding of lay perceptions of an important type of forensic science evidence (DNA and fingerprint comparisons), providing insight into how the perceived strength of that evidence varies as a function of the language forensic experts use to describe their findings. This novel application of mixture models illustrates how such models can be used, more generally, to explore the importance of juror characteristics in jury decision making.
期刊介绍:
Law, Probability & Risk is a fully refereed journal which publishes papers dealing with topics on the interface of law and probabilistic reasoning. These are interpreted broadly to include aspects relevant to the interpretation of scientific evidence, the assessment of uncertainty and the assessment of risk. The readership includes academic lawyers, mathematicians, statisticians and social scientists with interests in quantitative reasoning.
The primary objective of the journal is to cover issues in law, which have a scientific element, with an emphasis on statistical and probabilistic issues and the assessment of risk.
Examples of topics which may be covered include communications law, computers and the law, environmental law, law and medicine, regulatory law for science and technology, identification problems (such as DNA but including other materials), sampling issues (drugs, computer pornography, fraud), offender profiling, credit scoring, risk assessment, the role of statistics and probability in drafting legislation, the assessment of competing theories of evidence (possibly with a view to forming an optimal combination of them). In addition, a whole new area is emerging in the application of computers to medicine and other safety-critical areas. New legislation is required to define the responsibility of computer experts who develop software for tackling these safety-critical problems.