刑事证据评估的统计学习算法(LEARNING ALGORITHMS TO EVALUATE FORENSIC EVIDENCE)
报告人: Alicia Carriquiry, Iowa State University
时间:2018-05-21 16:00 ~ 17:00
地点:光华管理学院1号楼201室
Abstract:
The evaluation of evidence arises in criminal and civil judicial proceedings. In recent years, the scientific validity of most forensic analyses has been questioned, and there is now a push to develop the scientific and statistical underpinnings of various type of forensic tools. Pattern evidence including fingerprints, shoe out-sole impressions, striations on bullets and breech faces and others is particularly challenging, because this type of evidence is typically represented as an image, and does not lend itself to the traditional statistical modeling approach. Even numerical evidence such as the chemical composition of glass or paint is difficult to model, because it is often highly multi-dimensional.
In this talk, we focus on the question of source: do two evidence items have a common source? To answer this question, we develop non-parametric classification algorithms and show their application on glass and shoe out-sole impressions. We argue that learning algorithms enable forensic practitioners to quantify the degree of similarity between two items of evidence. Furthermore, by computing a score-based likelihood ratio, practitioners can assess the weight of the evidence in support of the prosecution’s or the defense’s propositions.
About the Speaker:
Alicia Carriquiry,爱荷华州立大学统计学杰出讲席教授、美国医学院院士、美国国家刑事侦探统计研究所主任。Carriquiry教授的主要研究方向包括贝叶斯方法、刑事侦探统计、营养和饮食评估、基因组学、法医学等问题。
Carriquiry教授是国际统计研究所的当选会士、美国统计协会会士、美国数理统计研究所会士,她也曾担任国际贝叶斯分析学会(ISBA)主席。