🤖 AI Summary
This study addresses the evaluation and selection of source-level likelihood ratio (LR) systems for forensic evidence-to-reference comparison tasks by proposing an integrated analytical framework that balances performance and practical feasibility. The authors employ strictly proper scoring rules to quantify how effectively each system updates Bayesian prior odds and present the first systematic comparison among specific-source feature-based, common-source anchored, and unanchored score-based LR approaches. Their findings reveal that specific-source feature-based LRs achieve the highest performance but incur substantial experimental costs, whereas common-source feature-based methods offer strong discriminative power with significantly reduced implementation complexity. All LR systems substantially outperform a baseline relying solely on prior odds. This work thus provides both theoretical grounding and practical guidance for selecting LR systems in forensic practice.
📝 Abstract
This paper studies expected performance and practical feasibility of the most commonly used classes of source-level likelihood-ratio (LR) systems when applied to a trace-reference comparison problem. The paper compares performance of these classes of LR systems (used to update prior odds) to each other and to the use of prior odds only, using strictly proper scoring rules as performance measures. It also explores practical feasibility of the classes of LR systems. The present analysis allows for a ranking of these classes of LR systems: from specific-source feature-based to common-source anchored or non-anchored score-based. A trade-off between performance and practical feasibility is observed, meaning that the best performing class of LR systems is the hardest to realise in practice, while the least performing class is the easiest to realise in practice. The other classes of LR systems are in between the two extremes. The one positive exception is a common-source feature-based LR system, with good performance and relatively low experimental demands. The paper also argues against the claim that some classes of LR systems should not be used, by showing that all systems have merit (when updating prior odds) over just using the prior odds (i.e. not using the LR system).