  In 2016, the European Network of Forensic Science Institutes (ENFSI)
published guidelines for the evaluation, interpretation and reporting of
scientific evidence. In the guidelines, ENFSI endorsed the use of the
likelihood ratio (LR) as a means to represent the probative value of most types
of evidence. While computing the value of a LR is practical in several forensic
disciplines, calculating an LR for pattern evidence such as fingerprints,
firearm and other toolmarks is particularly challenging because standard
statistical approaches are not applicable. Recent research suggests that
machine learning algorithms can summarize a potentially large set of features
into a single score which can then be used to quantify the similarity between
pattern samples. It is then possible to compute a score-based likelihood ratio
(SLR) and obtain an approximation to the value of the evidence, but research
has shown that the SLR can be quite different from the LR not only in size but
also in direction. We provide theoretical and empirical arguments that under
reasonable assumptions, the SLR can be a practical tool for forensic
evaluations.
