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Machine learning in forensic applications
Author(s) -
Carriquiry Alicia,
Hofmann Heike,
Tai Xiao Hui,
VanderPlas Susan
Publication year - 2019
Publication title -
significance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 21
eISSN - 1740-9713
pISSN - 1740-9705
DOI - 10.1111/j.1740-9713.2019.01252.x
Subject(s) - subjectivity , forensic science , library science , data science , artificial intelligence , computer science , engineering ethics , history , engineering , epistemology , archaeology , philosophy
The 2009 National Academy of Sciences report found pattern‐evidence disciplines to be rife with subjectivity. In the decade since, machine learning methods have been developed to try to address that issue. By Alicia Carriquiry, Heike Hofmann, Xiao Hui Tai and Susan VanderPlas.