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:
Perspectives and Opinion
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
Descripción
Sumario: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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