Ensemble of SLR Systems for Forensic Evidence

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Veneri, Federico
Ommen, Danica
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Center for Statistics and Applications in Forensic Evidence
The Center for Statistics and Applications in Forensic Evidence (CSAFE) carries out research on the scientific foundations of forensic methods, develops novel statistical methods and transfers knowledge and technological innovations to the forensic science community. We collaborate with more than 80 researchers and across six universities to drive solutions to support our forensic community partners with accessible tools, open-source databases and educational opportunities.
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The Department of Statistics seeks to teach students in the theory and methodology of statistics and statistical analysis, preparing its students for entry-level work in business, industry, commerce, government, or academia.

The Department of Statistics was formed in 1948, emerging from the functions performed at the Statistics Laboratory. Originally included in the College of Sciences and Humanities, in 1971 it became co-directed with the College of Agriculture.

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Score-based likelihood ratios (SLR) have been used as an alternative to feature-based likelihood ratios when the construction of a probabilistic model becomes challenging or infeasible [1]. Although SLR has been shown to provide an alternative way to present a numeric assessment of evidential strength, there are still concerns regarding their use in a forensic setting [2]. The SLR approach requires two key components. First, developing a (dis)similarity score, and second, estimating the distribution of the scores under both prosecutor and defense propositions. This process relies on the construction of pairwise comparisons in both stages. Previous work addresses the dependency on the sets used for producing SLR, how introducing perturbation to the sets can lead the forensic examiner to different conclusions, particularly the sensitivity during the second stage [3]. A second less explored dependence structure is produced because forensic glass evidence can be thought to be generated by a hierarchical model [4]. When a pairwise comparison is made, the same source enters the comparison multiple times, undermining the independence assumption often required by the methods used for developing a (dis)similarity score and estimating their distribution. We introduce an ensemble method for creating an SLR system that aims to reduce sensitivity to perturbation in the samples and remove the dependence structure. We create independent subsets by sampling only one comparison from each source to create base SLR systems, and then ensemble them in a final system that can be used in a later stage.
The following poster was presented at the 74th Annual Scientific Conference of the American Academy of Forensic Sciences (AAFS), Seattle, Washington, February 21-25, 2022. Posted with permission of CSAFE.