Department of Criminology, Law & Society, University of California, Irvine, CA 92617.
Proc Natl Acad Sci U S A. 2023 Oct 10;120(41):e2301844120. doi: 10.1073/pnas.2301844120. Epub 2023 Oct 2.
Forensic pattern analysis requires examiners to compare the patterns of items such as fingerprints or tool marks to assess whether they have a common source. This article uses signal detection theory to model examiners' reported conclusions (e.g., identification, inconclusive, or exclusion), focusing on the connection between the examiner's decision threshold and the probative value of the forensic evidence. It uses a Bayesian network model to explore how shifts in decision thresholds may affect rates and ratios of true and false convictions in a hypothetical legal system. It demonstrates that small shifts in decision thresholds, which may arise from contextual bias, can dramatically affect the value of forensic pattern-matching evidence and its utility in the legal system.
法医模式分析要求检验员比较指纹或工具痕迹等项目的模式，以评估它们是否来自同一来源。本文使用信号检测理论来构建检验员报告结论的模型（例如，鉴定、不确定或排除），重点关注检验员的决策阈值与法医证据的证明价值之间的联系。它使用贝叶斯网络模型来探索决策阈值的微小变化如何可能影响假设法律制度中真实和错误定罪的比率和比例。它表明，决策阈值的微小变化，可能源于背景偏差，会极大地影响法医模式匹配证据的价值及其在法律系统中的效用。
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