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Hierarchical Bayesian non-response models for error rates in forensic black-box studies.

Kori Khan1,2, Alicia Carriquiry1,2

  • 1Department of Statistics, Iowa State University, Ames, IA, USA.

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Summary
This summary is machine-generated.

Forensic science error rates in black-box studies are underestimated due to missing responses. New Bayesian models reveal actual error rates could be significantly higher, impacting legal system validity.

Keywords:
Bayesian inferenceblack-box studyforensic scienceitem non-responsenon-ignorable missingness

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Area of Science:

  • Forensic science
  • Statistical modeling
  • Criminal justice

Background:

  • Feature-based forensic disciplines like firearms and latent print analysis lack demonstrated scientific validity.
  • Black-box studies assess forensic validity but ignore high rates of examiner non-response or "don't know" answers.
  • Existing statistical analyses in these studies do not adequately address missing data, and data for adjustment is often unavailable.

Purpose of the Study:

  • To formally explore the impact of missing responses on error rate estimations in forensic black-box studies.
  • To propose hierarchical Bayesian models for adjusting error rates without requiring auxiliary data.
  • To provide a foundation for new methodologies addressing missingness in forensic error rate estimations.

Main Methods:

  • Utilized hierarchical Bayesian models adapted from small area estimation techniques.
  • Applied models to analyze data from forensic black-box studies, specifically accounting for non-response.
  • Compared error rate estimations with and without adjustments for missing or inconclusive decisions.

Main Results:

  • Current error rates reported as low as 0.4% could be at least 8.4% when accounting for non-response and counting inconclusives as correct.
  • Error rates could exceed 28% when inconclusives are treated as missing responses.
  • Demonstrated the significant underestimation of error rates in current black-box study analyses.

Conclusions:

  • Missingness in forensic black-box studies substantially impacts reported error rates.
  • Hierarchical Bayesian models offer a method to adjust for non-response without auxiliary data.
  • Further development and data sharing are crucial for robustly addressing missingness in forensic science validation.