A practical tool for information management in forensic decisions: Using Linear Sequential Unmasking-Expanded (LSU-E) in casework
Date
2022
Authors
Quigley-McBride, Adele
Dror, Itiel E.
Roy, Tiffany
Garrett, Brandon L.
Kukucka, Jeff
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Copyright 2022 The Authors
Abstract
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. Linear Sequential Unmasking (LSU; Dror et al., 2015) and LSU-Expanded (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.
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The following article is published as Quigley-McBride, Adele, Itiel E. Dror, Tiffany Roy, Brandon L. Garrett, and Jeff Kukucka. "A practical tool for information management in forensic decisions: Using Linear Sequential Unmasking-Expanded (LSU-E) in casework." Forensic Science International: Synergy 4 (2022): 100216. Posted with permission of CSAFE.