Machine learning-based Score Likelihood Ratios have been proposed as an alternative to traditional Likelihood Ratio and Bayes Factor to quantify the value of forensic evidence. Scores allow formulating comparisons using a lower-dimensional metric, which becomes relevant for complex evidence where developing a statistical model becomes challenging. Under this framework, a (dis)similarity score and its distribution under alternative propositions is estimated using pairwise comparison from a sample of the background population. These procedures often rely on the independence assumption, which is not met when the database consists of pairwise comparisons. To remedy this lack of independence, we introduce an ensembling approach that constructs training and estimation sets by sampling forensic sources, ensuring they are selected only once per set. Using these newly created datasets, we construct multiple base SLR systems and aggregate their information into a final score to quantify the value of evidence. We illustrate our approach under the common source problem and compare the performance of the ensembled system to the traditional approach.
This poster tied for second place in the Statistical Significance poster competition at the Joint Statistical Meetings.