Synthetic Anchoring Under the Specific Source Problem

ABSTRACT Source identification is an inferential problem that evaluates the likelihood of opposing propositions regarding the origin of items. The specific source problem refers to a situation where the researcher aims to assess if a particular source originated the items or if they originated from an alternative, unknown source. Score‐based likelihood ratios offer an alternative method to assess the relative likelihood of both propositions when formulating a probabilistic model is challenging or infeasible, as in the case of pattern evidence in forensic science. However, the lack of available data and the dependence structure created by the current procedure for generating learning instances can lead to reduced performance of score likelihood ratio systems. To address these issues, we propose a resampling plan that creates synthetic items to generate learning instances under the specific source problem. Simulation results show that our approach achieves a high level of agreement with an ideal scenario where data is not a limitation and learning instances are independent. We also present two applications in forensic sciences—handwriting and glass analysis—illustrating our approach with both a score‐based and a machine learning‐based score likelihood ratio system. These applications show that our method may outperform current alternatives in the literature.