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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
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.
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.
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