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The opacity myth: A response to Swofford & Champod (2022)

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 pa...

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Detalles Bibliográficos
Autores principales: Morrison, Geoffrey Stewart, Basu, Nabanita, Enzinger, Ewald, Weber, Philip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233202/
https://www.ncbi.nlm.nih.gov/pubmed/35762013
http://dx.doi.org/10.1016/j.fsisyn.2022.100275
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author Morrison, Geoffrey Stewart
Basu, Nabanita
Enzinger, Ewald
Weber, Philip
author_facet Morrison, Geoffrey Stewart
Basu, Nabanita
Enzinger, Ewald
Weber, Philip
author_sort Morrison, Geoffrey Stewart
collection PubMed
description 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.
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spelling pubmed-92332022022-06-26 The opacity myth: A response to Swofford & Champod (2022) Morrison, Geoffrey Stewart Basu, Nabanita Enzinger, Ewald Weber, Philip Forensic Sci Int Synerg Perspectives and Opinion 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. Elsevier 2022-06-19 /pmc/articles/PMC9233202/ /pubmed/35762013 http://dx.doi.org/10.1016/j.fsisyn.2022.100275 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Perspectives and Opinion
Morrison, Geoffrey Stewart
Basu, Nabanita
Enzinger, Ewald
Weber, Philip
The opacity myth: A response to Swofford & Champod (2022)
title The opacity myth: A response to Swofford & Champod (2022)
title_full The opacity myth: A response to Swofford & Champod (2022)
title_fullStr The opacity myth: A response to Swofford & Champod (2022)
title_full_unstemmed The opacity myth: A response to Swofford & Champod (2022)
title_short The opacity myth: A response to Swofford & Champod (2022)
title_sort opacity myth: a response to swofford & champod (2022)
topic Perspectives and Opinion
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233202/
https://www.ncbi.nlm.nih.gov/pubmed/35762013
http://dx.doi.org/10.1016/j.fsisyn.2022.100275
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