An overview of log likelihood ratio cost in forensic science – Where is it used and what values can we expect?

Q1 Social Sciences Forensic Science International: Synergy Pub Date : 2024-01-01 DOI:10.1016/j.fsisyn.2024.100466

Stijn van Lierop , Daniel Ramos , Marjan Sjerps , Rolf Ypma

{"title":"An overview of log likelihood ratio cost in forensic science – Where is it used and what values can we expect?","authors":"Stijn van Lierop , Daniel Ramos , Marjan Sjerps , Rolf Ypma","doi":"10.1016/j.fsisyn.2024.100466","DOIUrl":null,"url":null,"abstract":"<div>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 (Cllr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. Cllr = 0 indicates perfection while Cllr = 1 indicates an uninformative system. However, beyond this, what constitutes a “good” Cllr is unclear.Aiming to provide handles on when a Cllr is “good”, we studied 136 publications on (semi-)automated LR systems. Results show Cllr 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 Cllr remains stable. Noticeably, Cllr 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.</div>","PeriodicalId":36925,"journal":{"name":"Forensic Science International: Synergy","volume":"8 ","pages":"Article 100466"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589871X24000135/pdfft?md5=ba5837ba032d15cb5b24187d341f287f&pid=1-s2.0-S2589871X24000135-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forensic Science International: Synergy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589871X24000135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}

引用次数: 0

Abstract

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 (C_llr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. C_llr = 0 indicates perfection while C_llr = 1 indicates an uninformative system. However, beyond this, what constitutes a “good” C_llr is unclear.

Aiming to provide handles on when a C_llr is “good”, we studied 136 publications on (semi-)automated LR systems. Results show C_llr 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 C_llr remains stable. Noticeably, C_llr 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.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

法医学中的对数似然比成本概述--它用于何处，我们可以期望什么值？

越来越多的人支持用似然比（LR）来报告证据强度，对（半）自动化 LR 系统的兴趣也与日俱增。对数似然比成本（Cllr）是此类系统的常用指标，它对误导性的似然比（LR）进行惩罚，离 1 越远，惩罚越多。Cllr = 0 表示系统完美，而 Cllr = 1 则表示系统信息不全。我们研究了 136 篇关于（半）自动 LR 系统的出版物，旨在了解什么情况下 Cllr 是 "好 "的。结果表明，Cllr的使用在很大程度上取决于领域，例如，在DNA分析中就不存在Cllr。尽管随着时间的推移，关于自动 LR 系统的论文越来越多，但报告 Cllr 的比例却保持稳定。值得注意的是，Cllr 值缺乏明确的模式，取决于领域、分析和数据集。随着 LR 系统的普及，对它们进行比较变得至关重要。由于不同的研究使用不同的数据集，因此比较工作受到阻碍。我们主张使用公共基准数据集来推动这一领域的发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊