Law, Probability and Risk最新文献

How the work being done on statistical fingerprint models provides the basis for a much broader and greater impact affecting many areas within the criminal justice system 统计指纹模型方面的工作如何为影响刑事司法系统内许多领域的更广泛和更大的影响奠定基础

Law, Probability and Risk Pub Date : 2024-06-22 DOI: 10.1093/lpr/mgae008

David Stoney, Paul Stoney

{"title":"How the work being done on statistical fingerprint models provides the basis for a much broader and greater impact affecting many areas within the criminal justice system","authors":"David Stoney, Paul Stoney","doi":"10.1093/lpr/mgae008","DOIUrl":"https://doi.org/10.1093/lpr/mgae008","url":null,"abstract":"In the process of developing and improving statistical models to address flaws in the examination and interpretation of highly selective fingermarks, the groundwork is being laid for a much broader and greater impact. This impact will arise from the use of these same improved statistical methods to exploit information from the examination of fingermarks with lower degrees of selectivity—those fingermarks traditionally considered to be devoid of evidentiary value. To the contrary, research has shown that fingermarks of lower selectivity have much to offer. They occur very frequently: much more often than those assessed to be sufficient for inclusion in existing fingerprint examination processes. In individual cases, they occur in locations and numbers that can provide important new information for investigators and additional routes to further investigation. As evidence contributing to proving a case, they can provide detailed activity-level information and new avenues to address the relevance and probative value of other direct and circumstantial evidence. The broader application of fingerprint models to these traditionally unused fingermarks of lower selectivity needs to be specifically developed and implemented to realize the contributions and to responsibly manage the risks and benefits.","PeriodicalId":501426,"journal":{"name":"Law, Probability and Risk","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Misuse of statistical method results in highly biased interpretation of forensic evidence in 统计方法的滥用导致对法医证据的解释出现严重偏差。

Law, Probability and Risk Pub Date : 2024-01-01 DOI: 10.1093/lpr/mgad010

Michael Rosenblum, Elizabeth T. Chin, Elizabeth L Ogburn, Akihiko Nishimura, Daniel Westreich, Abhirup Datta, Susan Vanderplas, Maria Cuellar, William C. Thompson

引用次数: 0

Likelihood ratios for categorical count data with applications in digital forensics 分类计数数据的似然比在数字取证中的应用

Law, Probability and Risk Pub Date : 2022-12-23 DOI: 10.1093/lpr/mgac016

Rachel Longjohn, Padhraic Smyth, Hal S Stern

{"title":"Likelihood ratios for categorical count data with applications in digital forensics","authors":"Rachel Longjohn, Padhraic Smyth, Hal S Stern","doi":"10.1093/lpr/mgac016","DOIUrl":"https://doi.org/10.1093/lpr/mgac016","url":null,"abstract":"We consider the forensic context in which the goal is to assess whether two sets of observed data came from the same source or from different sources. In particular, we focus on the situation in which the evidence consists of two sets of categorical count data: a set of event counts from an unknown source tied to a crime and a set of event counts generated by a known source. Using a same-source versus different-source hypothesis framework, we develop an approach to calculating a likelihood ratio. Under our proposed model, the likelihood ratio can be calculated in closed form, and we use this to theoretically analyse how the likelihood ratio is affected by how much data is observed, the number of event types being considered, and the prior used in the Bayesian model. Our work is motivated in particular by user-generated event data in digital forensics, a context in which relatively few statistical methodologies have yet been developed to support quantitative analysis of event data after it is extracted from a device. We evaluate our proposed method through experiments using three real-world event datasets, representing a variety of event types that may arise in digital forensics. The results of the theoretical analyses and experiments with real-world datasets demonstrate that while this model is a useful starting point for the statistical forensic analysis of user-generated event data, more work is needed before it can be applied for practical use.","PeriodicalId":501426,"journal":{"name":"Law, Probability and Risk","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138535351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Likelihood ratio to evaluate handwriting evidence using similarity index 用相似度指数评价笔迹证据的似然比

Law, Probability and Risk Pub Date : 2022-11-28 DOI: 10.1093/lpr/mgac013

Jȩdrzej Wydra, Szymon Matuszewski

引用次数: 0

Interview with Professor Colin Aitken 采访科林·艾特肯教授

Law, Probability and Risk Pub Date : 2022-02-15 DOI: 10.1093/lpr/mgac001

Aitken C.

引用次数: 0

Priors neutral between the parties: the Batson motion in Idaho v. Ish 当事人之间的先前中立:爱达荷州诉伊什州的巴特森动议

Law, Probability and Risk Pub Date : 2022-02-08 DOI: 10.1093/lpr/mgab005

Kadane J.