Swofford H, Champod C
School of Criminal Justice, Forensic Science Institute, University of Lausanne, Switzerland.
Forensic Sci Int Synerg. 2022 Aug 5;5:100277. doi: 10.1016/j.fsisyn.2022.100277. eCollection 2022.
In Swofford & Champod (2022), we report the results of semi-structured interviews to various criminal justice stakeholders, including laboratory managers, prosecutors, defense attorneys, judges, and other academic scholars, on issues related to interpretation and reporting practices and the use of computational algorithms in forensic science within the American criminal justice system. Morrison et al. (2022) responded to that article claiming the interview protocol used a leading question with a false premise relating to the opaqueness of machine-learning methods. We disagree with the assertions of Morrison et al. (2022) and contend the premise to the question was relevant and appropriate.
在斯沃福德和尚波德(2022年)的研究中,我们向包括实验室管理人员、检察官、辩护律师、法官和其他学术学者在内的美国刑事司法系统中的各类刑事司法利益相关者汇报了半结构化访谈的结果,这些访谈围绕美国刑事司法系统内法医学中与解释和报告实践以及计算算法的使用相关的问题展开。莫里森等人(2022年)针对该文章做出回应,声称访谈协议使用了一个带有与机器学习方法不透明性相关的错误前提的引导性问题。我们不同意莫里森等人(2022年)的断言,并认为该问题的前提是相关且恰当的。