Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

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Widespread practice across the majority of branches of forensic science uses analytical methods based on human perception, and interpretive methods based on subjective judgement. These methods are non-transparent and are susceptible to cognitive bias, interpretation is often logically flawed, and forensic-evaluation systems are often not empirically validated. I describe a paradigm shift in which existing methods are replaced by methods based on relevant data, quantitative measurements, and statistical models; methods that are transparent and reproducible, are intrinsically resistant to cognitive bias, use the logically correct framework for interpretation of evidence (the likelihood-ratio framework), and are empirically validated under casework conditions.

Original languageEnglish
Article number100270
JournalForensic Science International: Synergy
Early online date18 May 2022
Publication statusPublished - May 2022

Bibliographical note

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

Funding: This research was supported by Research England's Expanding Excellence in England Fund as part of funding for the Aston Institute for Forensic Linguistics 2019–2023.


  • Forensic data science
  • Forensic science
  • Likelihood ratio
  • Paradigm shift
  • Validation


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