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 i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Forensic Science International: Synergy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589871X22000602 |
| _version_ | 1811178600982904832 |
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| author | Geoffrey Stewart Morrison Nabanita Basu Ewald Enzinger Philip Weber |
| author_facet | Geoffrey Stewart Morrison Nabanita Basu Ewald Enzinger Philip Weber |
| author_sort | Geoffrey Stewart Morrison |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T06:22:18Z |
| format | Article |
| id | doaj.art-dc7d04f97f8d4358be2ed639921a318b |
| institution | Directory Open Access Journal |
| issn | 2589-871X |
| language | English |
| last_indexed | 2024-04-11T06:22:18Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Forensic Science International: Synergy |
| spelling | doaj.art-dc7d04f97f8d4358be2ed639921a318b2022-12-22T04:40:33ZengElsevierForensic Science International: Synergy2589-871X2022-01-015100275The opacity myth: A response to Swofford & Champod (2022)Geoffrey Stewart Morrison0Nabanita Basu1Ewald Enzinger2Philip Weber3Corresponding author.; Forensic Data Science Laboratory, Aston University, Birmingham, UK; Forensic Evaluation Ltd, Birmingham, UKForensic Data Science Laboratory, Aston University, Birmingham, UKEduworks Corporation, Corvallis, OR, USA; Forensic Data Science Laboratory, Aston University, Birmingham, UKForensic Data Science Laboratory, Aston University, Birmingham, UKSwofford & 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.http://www.sciencedirect.com/science/article/pii/S2589871X22000602Forensic inferenceStatistical modelMachine learningArtificial intelligenceUnderstanding |
| spellingShingle | Geoffrey Stewart Morrison Nabanita Basu Ewald Enzinger Philip Weber The opacity myth: A response to Swofford & Champod (2022) Forensic Science International: Synergy Forensic inference Statistical model Machine learning Artificial intelligence Understanding |
| 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 amp champod 2022 |
| topic | Forensic inference Statistical model Machine learning Artificial intelligence Understanding |
| url | http://www.sciencedirect.com/science/article/pii/S2589871X22000602 |
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