不透明度神话：对斯沃福德和尚波德（2022年）的回应。
The opacity myth: A response to Swofford & Champod (2022).
作者信息
Morrison Geoffrey Stewart, Basu Nabanita, Enzinger Ewald, Weber Philip
机构信息
Forensic Data Science Laboratory, Aston University, Birmingham, UK.
Forensic Evaluation Ltd, Birmingham, UK.
出版信息
Forensic Sci Int Synerg. 2022 Jun 19;5:100275. doi: 10.1016/j.fsisyn.2022.100275. eCollection 2022.
Abstract
Swofford & Champod (2022) FSI Synergy article 100220 reports the results of semi-structured interviews that asked interviewees their views on probabilistic evaluation of forensic evidence in general, and probabilistic evaluation of forensic evidence performed using computational algorithms in particular. The interview protocol included a leading question based on the premise that machine-learning methods used in forensic inference are not understandable even to those who develop those methods. We contend that this is a false premise.
摘要
斯沃福德和尚波德（2022年）发表于《法医科学国际：协同作用》的第100220号文章报告了半结构化访谈的结果，这些访谈询问受访者对法医证据概率评估的总体看法，特别是对使用计算算法进行的法医证据概率评估的看法。访谈协议包含一个引导性问题，其前提是用于法医推断的机器学习方法即使对开发这些方法的人来说也是不可理解的。我们认为这是一个错误的前提。
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