论文标题
基于得分的似然比评估法医模式证据
Score-based likelihood ratios to evaluate forensic pattern evidence
论文作者
论文摘要
2016年，欧洲法医学研究所（ENFSI）网络发布了有关科学证据的评估，解释和报告的指南。在该指南中，ENFSI认可使用似然比（LR）作为代表大多数类型证据的证明价值的手段。尽管计算LR的价值在几个法医学科中是实用的，但是计算LR的模式证据，例如指纹，枪支和其他工具标志，这尤其具有挑战性，因为标准统计方法不适用。最近的研究表明，机器学习算法可以将潜在的大型特征汇总为单个分数，然后可以用来量化模式样本之间的相似性。然后，可以计算基于得分的似然比（SLR）并获得证据值的近似值，但是研究表明，SLR不仅与LR不仅在大小上而且在方向上都大不相同。我们提供了理论和经验论点，即在合理的假设下，SLR可以成为法医评估的实用工具。
In 2016, the European Network of Forensic Science Institutes (ENFSI) published guidelines for the evaluation, interpretation and reporting of scientific evidence. In the guidelines, ENFSI endorsed the use of the likelihood ratio (LR) as a means to represent the probative value of most types of evidence. While computing the value of a LR is practical in several forensic disciplines, calculating an LR for pattern evidence such as fingerprints, firearm and other toolmarks is particularly challenging because standard statistical approaches are not applicable. Recent research suggests that machine learning algorithms can summarize a potentially large set of features into a single score which can then be used to quantify the similarity between pattern samples. It is then possible to compute a score-based likelihood ratio (SLR) and obtain an approximation to the value of the evidence, but research has shown that the SLR can be quite different from the LR not only in size but also in direction. We provide theoretical and empirical arguments that under reasonable assumptions, the SLR can be a practical tool for forensic evaluations.
