TY - JOUR
T1 - The opacity myth: A response to Swofford & Champod (2022)
AU - Morrison, Geoffrey Stewart
AU - Basu, Nabanita
AU - Enzinger, Ewald
AU - Weber, Philip
N1 - © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY licence 4.0
PY - 2022/6/19
Y1 - 2022/6/19
N2 - 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. [Abstract copyright: © 2022 The Authors.]
AB - 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. [Abstract copyright: © 2022 The Authors.]
KW - Understanding
KW - Artificial intelligence
KW - Machine learning
KW - Statistical model
KW - Forensic inference
UR - https://www.sciencedirect.com/science/article/pii/S2589871X22000602?via%3Dihub
UR - http://www.scopus.com/inward/record.url?scp=85132740548&partnerID=8YFLogxK
U2 - 10.1016/j.fsisyn.2022.100275
DO - 10.1016/j.fsisyn.2022.100275
M3 - Article
C2 - 35762013
SN - 2589-871X
VL - 5
JO - Forensic Science International: Synergy
JF - Forensic Science International: Synergy
M1 - 100275
ER -