  Forensic science often involves the comparison of crime-scene evidence to a
known-source sample to determine if the evidence and the reference sample came
from the same source. Even as forensic analysis tools become increasingly
objective and automated, final source identifications are often left to
individual examiners' interpretation of the evidence. Each source
identification relies on judgements about the features and quality of the
crime-scene evidence that may vary from one examiner to the next. The current
approach to characterizing uncertainty in examiners' decision-making has
largely centered around the calculation of error rates aggregated across
examiners and identification tasks, without taking into account these
variations in behavior. We propose a new approach using IRT and IRT-like models
to account for differences among examiners and additionally account for the
varying difficulty among source identification tasks. In particular, we survey
some recent advances (Luby, 2019a) in the application of Bayesian psychometric
models, including simple Rasch models as well as more elaborate decision tree
models, to fingerprint examiner behavior.
