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Numerical likelihood ratios outputted by LR systems are often based on extrapolation: When to stop extrapolating?

Peter Vergeer1, Andrew van Es1, Arent de Jongh1

  • 1The Netherlands Forensic Institute, P.O. Box 24044, 2490 AA, The Hague, The Netherlands.

Science & Justice : Journal of the Forensic Science Society
|December 5, 2016
PubMed
Summary

Forensic science likelihood ratio (LR) systems risk extrapolation errors. This study proposes limits for LR values based on normalized Bayes error-rate and misleading LRs to ensure system reliability.

Keywords:
CalibrationLikelihood ratioNormalized Bayes errorStrength of evidenceValidation

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Area of Science:

  • Forensic science
  • Probability theory
  • Statistical modeling

Background:

  • Objective, automated systems for forensic material comparison generate numerical likelihood ratios (LRs).
  • LR systems rely on probability distributions trained by data, with potential inaccuracies when extrapolating beyond the data range, particularly in tail regions.

Purpose of the Study:

  • To address the sensitivity of LR systems to extrapolation errors.
  • To establish reliable limits for likelihood ratio outputs in forensic science applications.

Main Methods:

  • Utilizing the normalized Bayes error-rate.
  • Introducing misleading likelihood ratios with increasing strength to test system boundaries.

Main Results:

  • The proposed method allows for the determination of limit values (minimum and maximum) for LR outputs.
  • These limits are dependent on the size of the validation datasets used for training the probability distribution models.

Conclusions:

  • Establishing data-driven limits is crucial for the reliable application of automated LR systems in forensic science.
  • The normalized Bayes error-rate combined with misleading LR introduction offers a robust approach to defining these operational boundaries.