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

Anna Jeannette Leegwater; Peter Vergeer; Ivo Alberink; Leen V van der Ham; Judith van de Wetering; Rachid El Harchaoui; Wauter Bosma; Rolf J F Ypma; Marjan J Sjerps

doi:10.1016/j.forsciint.2024.111994

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

Forensic Sci Int. 2024 Apr:357:111994. doi: 10.1016/j.forsciint.2024.111994. Epub 2024 Mar 18.

Authors

Anna Jeannette Leegwater¹, Peter Vergeer¹, Ivo Alberink¹, Leen V van der Ham¹, Judith van de Wetering¹, Rachid El Harchaoui¹, Wauter Bosma¹, Rolf J F Ypma¹, Marjan J Sjerps²

Affiliations

¹ Netherlands Forensic Institute, PO Box 24044, The Hague 2490 AA, the Netherlands.
² Netherlands Forensic Institute, PO Box 24044, The Hague 2490 AA, the Netherlands; Korteweg-de Vries Institute for mathematics, University of Amsterdam, PO Box 94248, Amsterdam 1090 GE, the Netherlands. Electronic address: m.j.sjerps@uva.nl.

PMID: 38522325
DOI: 10.1016/j.forsciint.2024.111994

Abstract

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.

Keywords: Evidential strength; Forensic data; LR system; Likelihood ratio; Score-based; Validation.