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

Geoffrey Stewart Morrison, Nabanita Basu, Ewald Enzinger, Philip Weber

Research output: Contribution to journalArticlepeer-review


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. [Abstract copyright: © 2022 The Authors.]
Original languageEnglish
Article number100275
JournalForensic Science International: Synergy
Publication statusPublished - 19 Jun 2022

Bibliographical note

© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY licence 4.0


  • Understanding
  • Artificial intelligence
  • Machine learning
  • Statistical model
  • Forensic inference


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