The opacity myth: A response to Swofford & Champod (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. [Abstract copyright: © 2022 The Authors.]

Publication DOI: https://doi.org/10.1016/j.fsisyn.2022.100275
Divisions: College of Engineering & Physical Sciences
College of Business and Social Sciences > Aston Institute for Forensic Linguistics
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Additional Information: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY licence 4.0
Uncontrolled Keywords: Understanding,Artificial intelligence,Machine learning,Statistical model,Forensic inference
Publication ISSN: 2589-871X
Last Modified: 22 May 2024 07:21
Date Deposited: 13 Jul 2022 10:10
Full Text Link:
Related URLs: https://www.sci ... 0602?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-06-19
Accepted Date: 2022-06-13
Submitted Date: 2022-05-22
Authors: Morrison, Geoffrey Stewart (ORCID Profile 0000-0001-8608-8207)
Basu, Nabanita
Enzinger, Ewald
Weber, Philip (ORCID Profile 0000-0002-3121-9625)

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