The Experts below are selected from a list of 111 Experts worldwide ranked by ideXlab platform
Geoffrey Stewart Morrison - One of the best experts on this subject based on the ideXlab platform.
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reply paperwhat should a Forensic Practitioner s likelihood ratio be ii
Science & Justice, 2017Co-Authors: Geoffrey Stewart MorrisonAbstract:In the debate as to whether Forensic Practitioners should assess and report the precision of the strength of evidence statements that they report to the courts, I remain unconvinced by proponents of the position that only a subjectivist concept of probability is legitimate. I consider this position counterproductive for the goal of having Forensic Practitioners implement, and courts not only accept but demand, logically correct and scientifically valid evaluation of Forensic evidence. In considering what would be the best approach for evaluating strength of evidence, I suggest that the desiderata be (1) to maximise empirically demonstrable performance; (2) to maximise objectivity in the sense of maximising transparency and replicability, and minimising the potential for cognitive bias; and (3) to constrain and make overt the Forensic Practitioner's subjective-judgement based decisions so that the appropriateness of those decisions can be debated before the judge in an admissibility hearing and/or before the trier of fact at trial. All approaches require the Forensic Practitioner to use subjective judgement, but constraining subjective judgement to decisions relating to selection of hypotheses, properties to measure, training and test data to use, and statistical modelling procedures to use – decisions which are remote from the output stage of the analysis – will substantially reduce the potential for cognitive bias. Adopting procedures based on relevant data, quantitative measurements, and statistical models, and directly reporting the output of the statistical models will also maximise transparency and replicability. A procedure which calculates a Bayes factor on the basis of relevant sample data and reference priors is no less objective than a frequentist calculation of a likelihood ratio on the same data. In general, a Bayes factor calculated using uninformative or reference priors will be closer to a value of 1 than a frequentist best estimate likelihood ratio. The bound closest to 1 based on a frequentist best estimate likelihood ratio and an assessment of its precision will also, by definition, be closer to a value of 1 than the frequentist best estimate likelihood ratio. From a practical perspective, both procedures shrink the strength of evidence value towards the neutral value of 1. A single-value Bayes factor or likelihood ratio may be easier for the courts to handle than a distribution. I therefore propose as a potential practical solution, the use of procedures which account for imprecision by shrinking the calculated Bayes factor or likelihood ratio towards 1, the choice of the particular procedure being based on empirical demonstration of performance.
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Reply paperWhat should a Forensic Practitioner's likelihood ratio be? II☆
Science & Justice, 2017Co-Authors: Geoffrey Stewart MorrisonAbstract:In the debate as to whether Forensic Practitioners should assess and report the precision of the strength of evidence statements that they report to the courts, I remain unconvinced by proponents of the position that only a subjectivist concept of probability is legitimate. I consider this position counterproductive for the goal of having Forensic Practitioners implement, and courts not only accept but demand, logically correct and scientifically valid evaluation of Forensic evidence. In considering what would be the best approach for evaluating strength of evidence, I suggest that the desiderata be (1) to maximise empirically demonstrable performance; (2) to maximise objectivity in the sense of maximising transparency and replicability, and minimising the potential for cognitive bias; and (3) to constrain and make overt the Forensic Practitioner's subjective-judgement based decisions so that the appropriateness of those decisions can be debated before the judge in an admissibility hearing and/or before the trier of fact at trial. All approaches require the Forensic Practitioner to use subjective judgement, but constraining subjective judgement to decisions relating to selection of hypotheses, properties to measure, training and test data to use, and statistical modelling procedures to use – decisions which are remote from the output stage of the analysis – will substantially reduce the potential for cognitive bias. Adopting procedures based on relevant data, quantitative measurements, and statistical models, and directly reporting the output of the statistical models will also maximise transparency and replicability. A procedure which calculates a Bayes factor on the basis of relevant sample data and reference priors is no less objective than a frequentist calculation of a likelihood ratio on the same data. In general, a Bayes factor calculated using uninformative or reference priors will be closer to a value of 1 than a frequentist best estimate likelihood ratio. The bound closest to 1 based on a frequentist best estimate likelihood ratio and an assessment of its precision will also, by definition, be closer to a value of 1 than the frequentist best estimate likelihood ratio. From a practical perspective, both procedures shrink the strength of evidence value towards the neutral value of 1. A single-value Bayes factor or likelihood ratio may be easier for the courts to handle than a distribution. I therefore propose as a potential practical solution, the use of procedures which account for imprecision by shrinking the calculated Bayes factor or likelihood ratio towards 1, the choice of the particular procedure being based on empirical demonstration of performance.
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what should a Forensic Practitioner s likelihood ratio be
Science & Justice, 2016Co-Authors: Geoffrey Stewart Morrison, Ewald EnzingerAbstract:We argue that Forensic Practitioners should empirically assess and report the precision of their likelihood ratios. Once the Practitioner has specified the prosecution and defence hypotheses they have adopted, including the relevant population they have adopted, and has specified the type of measurements they will make, their task is to empirically calculate an estimate of a likelihood ratio which has a true but unknown value. We explicitly reject the competing philosophical position that the Forensic Practitioner's likelihood ratio should be based on subjective personal probabilities. Estimates of true but unknown values are based on samples and are subject to sampling uncertainty, and it is standard practice to report the degree of precision of such estimates. We discuss the dangers of not reporting precision to the courts, and the problems with an alternative approach which instead reports a verbal expression corresponding to a pre-specified range of likelihood ratio values. Reporting precision as an interval requires an arbitrary choice of coverage, e.g., a 95% or a 99% credible interval. We outline a normative framework which a trier of fact could employ to make non-arbitrary use of the results of Forensic Practitioners' empirical calculations of likelihood ratios and their precision.
Ewald Enzinger - One of the best experts on this subject based on the ideXlab platform.
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what should a Forensic Practitioner s likelihood ratio be
Science & Justice, 2016Co-Authors: Geoffrey Stewart Morrison, Ewald EnzingerAbstract:We argue that Forensic Practitioners should empirically assess and report the precision of their likelihood ratios. Once the Practitioner has specified the prosecution and defence hypotheses they have adopted, including the relevant population they have adopted, and has specified the type of measurements they will make, their task is to empirically calculate an estimate of a likelihood ratio which has a true but unknown value. We explicitly reject the competing philosophical position that the Forensic Practitioner's likelihood ratio should be based on subjective personal probabilities. Estimates of true but unknown values are based on samples and are subject to sampling uncertainty, and it is standard practice to report the degree of precision of such estimates. We discuss the dangers of not reporting precision to the courts, and the problems with an alternative approach which instead reports a verbal expression corresponding to a pre-specified range of likelihood ratio values. Reporting precision as an interval requires an arbitrary choice of coverage, e.g., a 95% or a 99% credible interval. We outline a normative framework which a trier of fact could employ to make non-arbitrary use of the results of Forensic Practitioners' empirical calculations of likelihood ratios and their precision.
Kim-kwang Raymond Choo - One of the best experts on this subject based on the ideXlab platform.
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A Forensically Sound Adversary Model for Mobile Devices
PLOS ONE, 2015Co-Authors: Quang Do, Ben Martini, Kim-kwang Raymond ChooAbstract:In this paper, we propose an adversary model to facilitate Forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon Forensic Practitioners is that of Forensic soundness. Our adversary model specifically considers and integrates the constraints of Forensic soundness on the adversary, in our case, a Forensic Practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of Forensic interest in both the external and internal storage of the mobile device.
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Chapter 14 – Conceptual evidence collection and analysis methodology for Android devices
arXiv: Computers and Society, 2015Co-Authors: Ben Martini, Quang Do, Kim-kwang Raymond ChooAbstract:Android devices continue to grow in popularity and capability meaning the need for a Forensically sound evidence collection methodology for these devices also increases. This chapter proposes a methodology for evidence collection and analysis for Android devices that is, as far as practical, device agnostic. Android devices may contain a significant amount of evidential data that could be essential to a Forensic Practitioner in their investigations. However, the retrieval of this data requires that the Practitioner understand and utilize techniques to analyze information collected from the device. The major contribution of this research is an in-depth evidence collection and analysis methodology for Forensic Practitioners.
Cathy Bruce - One of the best experts on this subject based on the ideXlab platform.
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Forensic epistemology: testing the reasoning skills of crime scene experts
Journal of the Canadian Society of Forensic Science, 2019Co-Authors: Mike Illes, Paul J. Wilson, Cathy BruceAbstract:AbstractIn recent years, crime scene analysis has been transitioning from being a technical discipline to being a scientific process. This progression is shifting the Forensic Practitioner examinin...
Quang Do - One of the best experts on this subject based on the ideXlab platform.
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A Forensically Sound Adversary Model for Mobile Devices
PLOS ONE, 2015Co-Authors: Quang Do, Ben Martini, Kim-kwang Raymond ChooAbstract:In this paper, we propose an adversary model to facilitate Forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon Forensic Practitioners is that of Forensic soundness. Our adversary model specifically considers and integrates the constraints of Forensic soundness on the adversary, in our case, a Forensic Practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of Forensic interest in both the external and internal storage of the mobile device.
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Chapter 14 – Conceptual evidence collection and analysis methodology for Android devices
arXiv: Computers and Society, 2015Co-Authors: Ben Martini, Quang Do, Kim-kwang Raymond ChooAbstract:Android devices continue to grow in popularity and capability meaning the need for a Forensically sound evidence collection methodology for these devices also increases. This chapter proposes a methodology for evidence collection and analysis for Android devices that is, as far as practical, device agnostic. Android devices may contain a significant amount of evidential data that could be essential to a Forensic Practitioner in their investigations. However, the retrieval of this data requires that the Practitioner understand and utilize techniques to analyze information collected from the device. The major contribution of this research is an in-depth evidence collection and analysis methodology for Forensic Practitioners.
