van Lierop Stijn, Ramos Daniel, Sjerps Marjan, Ypma Rolf
Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands.
AUDIAS Lab, Universidad Autonoma de Madrid, Escuela Politécnica Superior, Calle Francisco Tomàs y Valiente 11, 28049, Madrid, Spain.
Forensic Sci Int Synerg. 2024 Apr 17;8:100466. doi: 10.1016/j.fsisyn.2024.100466. eCollection 2024.
There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost () is a popular metric for such systems, penalizing misleading LRs further from 1 more. = 0 indicates perfection while = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" is unclear. Aiming to provide handles on when a is "good", we studied 136 publications on (semi-)automated LR systems. Results show use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting remains stable. Noticeably, values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field.
越来越多的人支持将证据强度报告为似然比(LR),并且对(半)自动化LR系统的兴趣也在增加。对数似然比代价()是此类系统常用的指标,对远离1的误导性LR惩罚更大。 = 0表示完美,而 = 1表示无信息价值的系统。然而,除此之外,什么构成“好的”并不明确。为了说明何时是“好的”,我们研究了136篇关于(半)自动化LR系统的出版物。结果表明,的使用严重依赖于领域,例如在DNA分析中不存在。尽管随着时间的推移,关于自动化LR系统的出版物越来越多,但报告的比例保持稳定。值得注意的是,值缺乏明确的模式,并且取决于领域、分析和数据集。随着LR系统变得越来越普遍,对它们进行比较变得至关重要。不同的研究使用不同的数据集阻碍了这一点。我们主张使用公共基准数据集来推动该领域的发展。