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

Anna Jeannette Leegwater1, Peter Vergeer1, Ivo Alberink1

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PubMed
Summary
This summary is machine-generated.

Forensic scientists can build automated likelihood ratio (LR) systems for consistent evidential strength assessment. This guide details the steps for creating validated LR systems using open-source Python software and glass data examples.

Keywords:
Evidential strengthForensic dataLR systemLikelihood ratioScore-basedValidation

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Area of Science:

  • Forensic Science
  • Statistical Analysis
  • Computational Methods

Background:

  • Likelihood Ratios (LRs) are crucial for quantifying evidential strength in forensic casework.
  • Automated LR systems offer a standardized approach for consistent evaluation when dealing with similar case questions and analysis methods.

Purpose of the Study:

  • To provide forensic practitioners with a practical overview of building validated Likelihood Ratio (LR) systems.
  • To guide users through the necessary steps, from data preparation to system validation.

Main Methods:

  • The paper outlines a systematic process for developing an LR system, which takes observations as input and outputs an LR.
  • An accompanying notebook demonstrates each step using glass evidence data.
  • The demonstration utilizes open-source Python software developed by the Netherlands Forensic Institute (NFI).

Main Results:

  • The study presents a clear methodology for constructing automated LR systems.
  • The provided example illustrates the practical application of the described steps and software.

Conclusions:

  • Building validated LR systems is feasible for forensic casework with repetitive questions and methods.
  • The presented approach and accompanying open-source tools empower practitioners to develop their own LR systems.