Shining a Light on Forensic Black-Box Studies

被引:7
|
作者
Khan, Kori [1 ,2 ]
Carriquiry, Alicia. L. L. [1 ,2 ]
机构
[1] Iowa State Univ, Dept Stat, Ames, IA 50011 USA
[2] Iowa State Univ, Ctr Stat & Applicat Forens Evidence CSAFE, Ames, IA USA
来源
STATISTICS AND PUBLIC POLICY | 2023年 / 10卷 / 01期
关键词
Criminal justice; Experimental design; Forensic science; Non-ignorable missingness; Sampling bias; MULTIPLE IMPUTATION; MISSING DATA; NONRESPONSE; INFERENCE; BULLET; BIAS;
D O I
10.1080/2330443X.2023.2216748
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
Forensic science plays a critical role in the United States criminal legal system. For decades, many feature-based fields of forensic science, such as firearm and toolmark identification, developed outside the scientific community's purview. The results of these studies are widely relied on by judges nationwide. However, this reliance is misplaced. Black-box studies to date suffer from inappropriate sampling methods and high rates of missingness. Current black-box studies ignore both problems in arriving at the error rate estimates presented to courts. We explore the impact of each type of limitation using available data from black-box studies and court materials. We show that black-box studies rely on unrepresentative samples of examiners. Using a case study of a popular ballistics study, we find evidence that these nonrepresentative samples may commit fewer errors than the wider population from which they came. We also find evidence that the missingness in black-box studies is non-ignorable. Using data from a recent latent print study, we show that ignoring this missingness likely results in systematic underestimates of error rates. Finally, we offer concrete steps to overcome these limitations. for this article areavailable online.
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页数:11
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