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Fingerprint examiners appear to be reluctant to adopt probabilistic reasoning, statistical models, and empirical validation. The rate of adoption of the likelihood-ratio framework by fingerprint practitioners appears to be near zero. A factor in the reluctance to adopt the likelihood-ratio framework may be a perception that it would require a radical change in practice. The present paper proposes a small step that would require minimal changes to current practice. It proposes and demonstrates a method to convert traditional fingerprint-examination outputs ("identification", "inconclusive", "exclusion") to well-calibrated Bayes factors. The method makes use of a beta-binomial model, and both uninformative and informative priors., Comment: 2022 manuscript

Logistic-regression calibration and fusion are potential steps in the calculation of forensic likelihood ratios. The present paper provides a tutorial on logistic-regression calibration and fusion at a practical conceptual level with minimal mathematical complexity. A score is log-likelihood-ratio like in that it indicates the degree of similarity of a pair of samples while taking into consideration their typicality with respect to a model of the relevant population. A higher-valued score provides more support for the same-origin hypothesis over the different-origin hypothesis than does a lower-valued score; however, the absolute values of scores are not interpretable as log likelihood ratios. Logistic-regression calibration is a procedure for converting scores to log likelihood ratios, and logistic-regression fusion is a procedure for converting parallel sets of scores from multiple forensic-comparison systems to log likelihood ratios. Logistic-regression calibration and fusion were developed for automatic speaker recognition and are popular in forensic voice comparison. They can also be applied in other branches of forensic science, a fingerprint/fingermark example is provided., Comment: 26 pages, 11 figures

Lennard (2013) [Fingerprint identification: how far have we come? Aus J Forensic Sci. doi:10.1080/00450618.2012.752037] proposes that the numeric output of statistical models should not be presented in court (except "if necessary" / "if required"). Instead he argues in favour of an "expert opinion" which may be informed by a statistical model but which is not itself the output of a statistical model. We argue that his proposed procedure lacks the transparency, the ease of testing of validity and reliability, and the relative robustness to cognitive bias that are the strengths of a likelihood-ratio approach based on relevant data, quantitative measurements, and statistical models, and that the latter is therefore preferable.

This chapter describes a number of signal-processing and statistical-modeling techniques that are commonly used to calculate likelihood ratios in human-supervised automatic approaches to forensic voice comparison. Techniques described include mel-frequency cepstral coefficients (MFCCs) feature extraction, Gaussian mixture model - universal background model (GMM-UBM) systems, i-vector - probabilistic linear discriminant analysis (i-vector PLDA) systems, deep neural network (DNN) based systems (including senone posterior i-vectors, bottleneck features, and embeddings / x-vectors), mismatch compensation, and score-to-likelihood-ratio conversion (aka calibration). Empirical validation of forensic-voice-comparison systems is also covered. The aim of the chapter is to bridge the gap between general introductions to forensic voice comparison and the highly technical automatic-speaker-recognition literature from which the signal-processing and statistical-modeling techniques are mostly drawn. Knowledge of the likelihood-ratio framework for the evaluation of forensic evidence is assumed. It is hoped that the material presented here will be of value to students of forensic voice comparison and to researchers interested in learning about statistical modeling techniques that could potentially also be applied to data from other branches of forensic science., Comment: Morrison, G.S., Enzinger, E., Ramos, D., Gonz\'alez-Rodr\'iguez, J., Lozano-D\'iez, A. (2020). Statistical models in forensic voice comparison. In Banks, D.L., Kafadar, K., Kaye, D.H., Tackett, M. (Eds.) Handbook of Forensic Statistics (Ch. 20, pp. 451-497). Boca Raton, FL: CRC

A press release from the National Institute of Standards and Technology (NIST)could potentially impede progress toward improving the analysis of forensic evidence and the presentation of forensic analysis results in courts in the United States and around the world. "NIST experts urge caution in use of courtroom evidence presentation method" was released on October 12, 2017, and was picked up by the phys.org news service. It argues that, except in exceptional cases, the results of forensic analyses should not be reported as "likelihood ratios". The press release, and the journal article by NIST researchers Steven P. Lund & Harri Iyer on which it is based, identifies some legitimate points of concern, but makes a strawman argument and reaches an unjustified conclusion that throws the baby out with the bathwater.

The present letter to the editor is one in a series of publications discussing the formulation of hypotheses (propositions) for the evaluation of strength of forensic evidence. In particular, the discussion focusses on the issue of what information may be used to define the relevant population specified as part of the different-speaker hypothesis in forensic voice comparison. The previous publications in the series are: Hicks et al. 2015 ; Morrison et al. (2016) ; Hicks et al. (2017) . The latter letter to the editor mostly resolves the apparent disagreement between the two groups of authors. We briefly discuss one outstanding point of apparent disagreement, and attempt to correct a misinterpretation of our earlier remarks. We believe that at this point there is no actual disagreement, and that both groups of authors are calling for greater collaboration in order to reduce the likelihood of future misunderstandings.

A group of approaches for calculating forensic likelihood ratios first calculates scores which quantify the degree of difference or the degree of similarity between pairs of samples, then converts those scores to likelihood ratios. In order for a score-based approach to produce a forensically interpretable likelihood ratio, however, in addition to accounting for the similarity of the questioned sample with respect to the known sample, it must also account for the typicality of the questioned sample with respect to the relevant population. The present paper explores a number of score-based approaches using different types of scores and different procedures for converting scores to likelihood ratios. Monte Carlo simulations are used to compare the output of these approaches to true likelihood-ratio values calculated on the basis of the distribution specified for a simulated population. The inadequacy of approaches based on similarity-only or difference-only scores is illustrated, and the relative performance of different approaches which take account of both similarity and typicality is assessed., Comment: 59 pages. A version of this paper was presented as: Morrison G.S., Enzinger E., Forensic likelihood ratios should not be based on similarity scores or difference scores, 9th International Conference on Forensic Inference and Statistics (ICFIS), August 2014, Leiden, The Netherlands