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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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. |
format | Online Article Text |
id | pubmed-9233202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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