From data to a validated score-based LR system: A practitioner’s guide

• Published in: Forensic science international 2024-04, Vol.357, p.111994-111994, Article 111994
• Main Authors: Leegwater, Anna Jeannette, Vergeer, Peter, Alberink, Ivo, van der Ham, Leen V., van de Wetering, Judith, El Harchaoui, Rachid, Bosma, Wauter, Ypma, Rolf J.F., Sjerps, Marjan J.
• Format: Article
• Language: English
• Subjects: automation; Datasets; Evidential strength; Forensic data; Forensic science; forensic sciences; glass; Likelihood ratio; LR system; Open source software; Python; Score-based; Validation; Variables
• ISSN: 0379-0738; 1872-6283
• DOI: 10.1016/j.forsciint.2024.111994

Likelihood ratios (LRs) are a useful measure of evidential strength. In forensic casework consisting of a flow of cases with essentially the same question and the same analysis method, it is feasible to construct an ‘LR system’, that is, an automated procedure that has the observations as input and an LR as output. This paper is aimed at practitioners interested in building their own LR systems. It gives an overview of the different steps needed to get to a validated LR system from data. The paper is accompanied by a notebook that illustrates each step with an example using glass data. The notebook introduces open-source software in Python constructed by NFI (Netherlands Forensic Institute) data scientists and statisticians. [Display omitted] •We present a procedure to transform raw data into a validated LR system for forensic casework.•Follow 8 easy-to-understand steps, each illustrated with forensic example data.•Technical details and current debates are explained in an appendix.•Explore LiR, the Python-based open-source library for LR calculations, through an accompanying notebook.•Build your own LR system using your data with this comprehensive set of tools!.