Quigley-McBride Adele, Dror Itiel E, Roy Tiffany, Garrett Brandon L, Kukucka Jeff
Wilson Center for Science and Justice, Duke University School of Law, United States.
University College London, United Kingdom.
Forensic Sci Int Synerg. 2022 Feb 20;4:100216. doi: 10.1016/j.fsisyn.2022.100216. eCollection 2022.
Forensic analysts often receive information from a multitude of sources. Empirical work clearly demonstrates that biasing information can affect analysts' decisions, and that the order in which task-relevant information is received impacts human cognition and decision-making. (LSU; Dror et al., 2015) and (LSU-E; Dror & Kukucka, 2021) are examples of research-based procedural frameworks to guide laboratories' and analysts' consideration and evaluation of case information. These frameworks identify parameters-such as objectivity, relevance, and biasing power-to prioritize and optimally sequence information for forensic analyses. Moreover, the LSU-E framework can be practically incorporated into any forensic discipline to improve decision quality by increasing the repeatability, reproducibility, and transparency of forensic analysts' decisions, as well as reduce bias. Future implementation of LSU and LSU-E in actual forensic casework can be facilitated by concrete guidance. We present here a practical worksheet designed to bridge the gap between research and practice by facilitating the implementation of LSU-E.
法医分析师常常从众多来源获取信息。实证研究清楚地表明，有偏差的信息会影响分析师的决策，而且接收与任务相关信息的顺序会影响人类的认知和决策。（路易斯安那州立大学；德罗尔等人，2015年）以及（路易斯安那州立大学扩展版；德罗尔和库库卡，2021年）是基于研究的程序框架示例，用于指导实验室和分析师对案件信息的考量与评估。这些框架确定了诸如客观性、相关性和偏差影响力等参数，以便为法医分析对信息进行优先排序并优化排序。此外，路易斯安那州立大学扩展版框架可以切实纳入任何法医学科，通过提高法医分析师决策的可重复性、可再现性和透明度来提升决策质量，并减少偏差。具体的指导能够推动路易斯安那州立大学框架和路易斯安那州立大学扩展版框架在实际法医案件工作中的未来应用。我们在此展示一份实用工作表，旨在通过推动路易斯安那州立大学扩展版框架的应用来弥合研究与实践之间的差距。
