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Taroni et al. (2016) discuss the controversial issue of parameter uncertainty in the context of forensic evidence evaluation. Although we share with the authors the main idea that the likelihood ratio (LR) framework is the best method for evaluating forensic evidence, we have a different view on this issue. The core question is: does itmake sense to consider the uncertainty attached to a calculated value of the LR, and consequently, should we report a single value for the LR or in addition address its uncertainty? Taroni et al. (2016) argue for reporting a single value based on a 'full-Bayesian'approach, and accuse anyone who considers the uncertainty of an LR of 'misconception of basic principles'and 'abuse of language'. However, their arguments presented as facts or logic are in fact choices or opinions. Furthermore, reporting a single number for the LR deprives the legal justice system of essential information needed to assess the reliability of the evidence. Therefore, we argue that forensic scientists should not only report an LR value, but also address its uncertainty and we explain why this is not a misconception or abuse of language. [ABSTRACT FROM AUTHOR]

In this article we respond to Dawid, Balding, Triggs and Buckleton. [ABSTRACT FROM PUBLISHER]

Most of the forensic evidence evaluations given activity level propositions are centered around an item which is assumed to be linked to an alleged activity. However, the relation between an item of interest and an activity may be contested. This study presents a template Bayesian network (BN) for the evaluation of transfer evidence given activity level propositions considering a dispute about the relation of an item to one or more activities. The template BN includes a set of association propositions that enables the combined evaluation of evidence concerning alleged activities of the suspect and evidence concerning the use of an alleged item in those activities. Since the two types of evidence are often from different forensic disciplines, the BN is especially useful in interdisciplinary casework. Throughout the paper, we use a fictive case example that captures the essence of cases for which the template model can be used. The template BN provides a flexible starting point that can be adapted to specific case situations and supports structured probabilistic reasoning by a forensic scientist., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost ( C llr ) is a popular metric for such systems, penalizing misleading LRs further from 1 more. C llr  = 0 indicates perfection while C llr  = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" C llr is unclear. Aiming to provide handles on when a C llr is "good", we studied 136 publications on (semi-)automated LR systems. Results show C llr use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting C llr remains stable. Noticeably, C llr values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This paper presents a collection of idioms that is useful for modeling activity level evaluations in forensic science using Bayesian networks. The idioms are categorized into five groups: cause-consequence idioms, narrative idioms, synthesis idioms, hypothesis-conditioning idioms, and evidence-conditioning idioms. Each category represents a specific modeling objective. Furthermore, we support the use of an idiom-based approach and emphasize the relevance of our collection by combining several of the presented idioms to create a more comprehensive template model. This model can be used in cases involving transfer evidence and disputes over the actor and/or activity. Additionally, we cite literature that employs idioms in template models or case-specific models, providing the reader with examples of their use in forensic casework., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Tire marks are an important type of forensic evidence as they are frequently encountered at crime scenes. When the tires of a suspect's car are compared, the evidence can be very strong if so-called 'acquired features' are observed to correspond. When only 'class characteristics' such as parts of the tire pattern are observed to correspond, it is obvious that many other tires will exist that also correspond, and so this evidence is usually considered very weak or is simply ignored. Like Benedict et al. (2014) we argue that such evidence can still be strong and should be taken into account. We describe a method for assessing the evidential strength of a set of corresponding class characteristics by presenting a case example from the Netherlands in which tire marks were obtained. Only part of two different tire patterns were visible, in combination with measurements on the axes width. Suitable databases were found already existing and accessible to forensic experts. We show how such data can be used to quantify the strength of evidence and how it can be reported. We also show how the risk of bias due to information surrounding the case may be minimized in cases like this. Our 'blind' procedure enables the expert to report a correspondence between class features in a more convincing way than standard procedures allow. In the particular exemplar case quite strong evidence was obtained, which was accepted and used by the Dutch court. We generalize this procedure for quantifying the evidential value of an expert's opinion of a correspondence. This examination procedure can be applied directly to other types of pattern evidence such as shoeprints, fingerprints, or images. Furthermore, it is 'blind' in the sense that the risk of contextual bias is minimized., Competing Interests: Declarations of interest none., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Evaluating evidence and providing opinions are at the heart of forensic science, and forensic experts are expected to provide opinions that are based on logically sound and transparent scientific reasoning, and that honour the boundaries of their area of expertise. In order to meet these objectives, many fields of science explicitly apply Bayes' theorem, which describes the logically correct way to update probabilities on the basis of observations. Making a distinction between 'investigative' and evaluative' modes of operating helps to implement the theorem into daily casework. Use of these principles promotes the logic and transparency of the reasoning that leads to expert's opinion and helps the expert to stay within her remit. Despite these important benefits, forensic pathology seems slow to adopt these principles. In this article, we explore this issue and suggest a way forward. We start with a short introduction to Bayes' theorem and its benefits, followed by a discussion of why its application is actually second nature to medical practitioners. We then discuss the difference between investigative and evaluative opinions, and how they enable the forensic pathologist to reconcile Bayes' theorem with the different phases of a forensic investigation. Throughout the text, practical examples illustrate the various ways in which the logically correct way of evidence interpretation can be implemented, and how it may help the forensic pathologist to provide an appropriate and relevant opinion., (© 2021. Crown.)

Forensic soil comparisons can be of high evidential value in a forensic case, but become complex when multiple methods and factors are considered. Bayesian networks are well suited to support forensic practitioners in complex casework. This study discusses the structure of a Bayesian network, elaborates on the in- and output data and evaluates two examples, one using source level propositions and one using activity level propositions. These examples can be applied as a template to construct a case specific network and can be used to assess sensitivity of the target output to different factors and identify avenues for research., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

A new method for the evaluation of duct tape ends is proposed. This method is based on the breaks of the loops in the warp yarns, when duct tape with a scrim of chain-stitched warp yarns and weft-insertion is torn. After tearing, the loop at the end of each warp yarn can be in one of four states: open, closed, complex or missing. Additionally, the horizontal position of each warp yarn can be expressed in terms of weft yarns. The evidential strength of these loopbreaking patterns is expressed in terms of likelihood ratios. We construct a likelihood ratio system to determine these likelihood ratios. This consists of three dynamic Bayesian networks, which are based on the main assumption that the loopbreaking patterns are a stochastic process which comply to the Markov property. A dataset is used to train and test the LR-system. Based on these results, it is found that the loopbreaking patterns contain very strong evidence. More data, especially on observation errors, is needed to evaluate the system further., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Numerical likelihood-ratio (LR) systems aim to calculate evidential strength for forensic evidence evaluation. Calibration of such LR-systems is essential: one does not want to over- or understate the strength of the evidence. Metrics that measure calibration differ in sensitivity to errors in calibration of such systems. In this paper we compare four calibration metrics by a simulation study based on Gaussian Log LR-distributions. Three calibration metrics are taken from the literature (Good, 1985; Royall, 1997; Ramos and Gonzalez-Rodriguez, 2013) [1-3], and a fourth metric is proposed by us. We evaluated these metrics by two performance criteria: differentiation (between well- and ill-calibrated LR-systems) and stability (of the value of the metric for a variety of well-calibrated LR-systems). Two metrics from the literature (the expected values of LR and of 1/LR, and the rate of misleading evidence stronger than 2) do not behave as desired in many simulated conditions. The third one (C llr cal ) performs better, but our newly proposed method (which we coin devPAV) is shown to behave equally well to clearly better under almost all simulated conditions. On the basis of this work, we recommend to use both devPAV and C llr cal to measure calibration of LR-systems, where the current results indicate that devPAV is the preferred metric. In the future external validity of this comparison study can be extended by simulating non-Gaussian LR-distributions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In their paper "The evaluation of evidence for microspectrophotometry data using functional data analysis", in FSI 305, Aitken et al. present a likelihood-ratio (LR) system for their data. We show the values generated by this system cannot be interpreted as LRs: they are ill-calibrated and should be interpreted as discriminating scores. We demonstrate how to transform the scores to well-calibrated LRs using a post-hoc calibrating step. Also, we address criticisms of calibration posited by Aitken et al. We conclude by noting that ill-calibrated LR-values are misleadingly small or large. Therefore calibration should be measured and, if necessary, corrected for. The corrected LR-values (instead of the discriminating scores) can be used to update the prior odds in Bayes rule., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Sampling strategy is one of the deciding factors in DNA typing success rates. Small amounts of bodily fluid traces and (skin) contact traces are currently not visualized in standard forensic practice. Trace recovery is usually based on the information available in a particular case and on the experience and 'forensic common sense' applied by the trace recovery expert. Interactions between an offender and a victim may have characteristic features, resulting in specific trace patterns. Understanding these interactions, and their resulting trace patterns, might improve crime related trace recovery as well as DNA typing success rates. In this study, we examined the interactions between offender and victim when a body has been relocated from one position/location to another. The contact between the hands of the offender and the body of the victim was visualized using a fluorescent dye in a lotion that was applied to the hands of the individual undertaking the relocation. The contact locations were scored and patterns were analyzed based on both victim and offender characteristics (height, weight, age, gender). The resulting patterns were compared to current trace recovery practices in the Netherlands. The results of this large-scale study facilitate evidence-based sampling supporting both investigative and evaluative forensic examinations., (Copyright © 2018 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.)

In this article, the performance of a score-based likelihood ratio (LR) system for comparisons of fingerprints with fingermarks is studied. The system is based on an automated fingerprint identification system (AFIS) comparison algorithm and focuses on fingerprint comparisons where the fingermarks contain 6-11 minutiae. The hypotheses under consideration are evaluated at the level of the person, not the finger. The LRs are presented with bootstrap intervals indicating the sampling uncertainty involved. Several aspects of the performance are measured: leave-one-out cross-validation is applied, and rates of misleading evidence are studied in two ways. A simulation study is performed to study the coverage of the bootstrap intervals. The results indicate that the evidential strength for same source comparisons that do not meet the Dutch twelve-point standard may be substantial. The methods used can be generalized to measure the performance of score-based LR systems in other fields of forensic science., (© 2017 American Academy of Forensic Sciences.)

In de Zoete et al. (2015) a framework for the evaluation of evidence when an individual is a suspect of two separate offenses (based on Evett et al., 2006) is implemented using a Bayesian network. Here, we extend this to situations with multiple offenders. When we have multiple offenders, new questions arise: (1) Can we distinguish between the offenders, even if we do not know their identity? (2) Do we know that certain pieces of evidence originate from the same person? (3) Do we know the number of offenders? With the aid of a mock case example, we show that such subtle differences between situations can lead to substantially different conclusions in terms of posterior probabilities of a certain suspect being one of the offenders in a particular crime. We reach our conclusions by constructing appropriate Bayesian networks for each situation. Although we find it undesirable that Bayesian networks are demonstrated in court, they can be very helpful in guiding expert and legal reasoning, identifying pitfalls and assist in preventing them. Bayesian networks can be used as a tool to understand how the different pieces of evidence influence each others evidential value, and the probabilities of the hypotheses of interest., (Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.)

In forensic casework, evidence regarding the type of cell material contained in a stain can be crucial in determining what happened. For example, a DNA match in a sexual offense can become substantially more incriminating when there is evidence supporting that semen cells are present. Besides the question which cell types are present in a sample, also the question who donated what (association) is very relevant. This question is surprisingly difficult, even for stains with a single donor. The evidential value of a DNA profile needs to be combined with knowledge regarding the specificity and sensitivity of cell type tests. This, together with prior probabilities for the different donor-cell type combinations, determines the most likely combination. We present a Bayesian network that can assist in associating donors and cell types. A literature overview on the sensitivity and specificity of three cell type tests (PSA test for seminal fluid, RSID saliva and RSID semen) is helpful in assigning conditional probabilities. The Bayesian network is linked with a software package for interpreting mixed DNA profiles. This allows for a sensitivity analysis that shows to what extent the conclusion depends on the quantity of available research data. This can aid in making decisions regarding further research. It is shown that the common assumption that an individual (e.g. the victim) is one of the donors in a mixed DNA profile can have unwanted consequences for the association between donors and cell types., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Two approaches were investigated to discriminate between bell peppers of different geographic origins. Firstly, δ(18)O fruit water and corresponding source water were analyzed and correlated to the regional GNIP (Global Network of Isotopes in Precipitation) values. The water and GNIP data showed good correlation with the pepper data, with constant isotope fractionation of about -4. Secondly, compound-specific stable hydrogen isotope data was used for classification. Using n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based classification, using the kernel-density smoothed data, were developed to discriminate between peppers from different origins. Both methods were evaluated using the δ(2)H values and n-alkanes relative composition as variables. Misclassification rates were calculated using a Monte-Carlo 5-fold cross-validation procedure. Comparable overall classification performance was achieved, however, the two methods showed sensitivity to different samples. The combined values of δ(2)H IRMS, and complimentary information regarding the relative abundance of four main alkanes in bell pepper fruit water, has proven effective for geographic origin discrimination. Evaluation of the rarity of observing particular ranges for these characteristics could be used to make quantitative assertions regarding geographic origin of bell peppers and, therefore, have a role in verifying compliance with labeling of geographical origin., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: The incidence of hepatitis E virus (HEV) infection in the Netherlands is high. Blood donors are not routinely screened for HEV infection, but since January 2013, donations used for the production of solvent/detergent (S/D)-treated plasma have been screened for HEV RNA., Study Design and Methods: Donations were screened for HEV RNA in pools of 96 and 192 donations. In addition, all donations made between 60 days before and after each HEV RNA-positive donation were tested individually for HEV RNA and anti-HEV immunoglobulin G., Results: The screening of 59,474 donations between January 2013 and December 2014 resulted in identification of 45 HEV RNA-positive donations (0.076%) from 41 donors. HEV RNA loads ranged from 80 to 2.3 × 10(6) IU/mL. The number of positive donations increased significantly over time (p = 0.03). Thirty-three of 90 donations made up to 60 days before or after HEV RNA-positive donations were positive when tested individually, while they had not been detected in the pool screening. The mean duration of HEV viremia in the healthy blood donor is estimated to be 68 days., Conclusion: The incidence of HEV infection in the Netherlands is high and increased during the study period. In 2013 and 2014, HEV RNA was detected in 1 per 762 donations intended for production of S/D plasma., (© 2015 AABB.)

DNA profiles can be used as evidence to distinguish between possible donors of a crime stain. In some cases, both the prosecution and the defence claim that the cell material was left by the suspect but they dispute which cell type was left behind. For example, in sexual offense cases the prosecution could claim that the sample contains semen cells where the defence argues that the sample contains skin cells. In these cases, traditional methods (e.g. a phosphatase test) can be used to examine the cell type contained in the sample. However, there are some drawbacks when using these methods. For instance, many of these techniques need to be carried out separately for each cell type and each of them requires part of the available sample, which reduces the amount that can be used for DNA analysis. Another option is messenger RNA (mRNA) evidence. mRNA expression levels vary among cell types and can be used to make (probability) statements about the cell type(s) present in a sample. Existing methods for the interpretation of RNA profiles as evidence for the presence of certain cell types aim at making categorical statements. Such statements limit the possibility to report the associated uncertainty. Some of these existing methods will be discussed. Most notably, a method based on a 'n/2' scoring rule (Lindenbergh et al.) and a method using marker values and cell type scoring thresholds (Roeder et al.). From a statistical point of view, a probabilistic approach is the most obvious choice. Two approaches (multinomial logistic regression and naïve Bayes') are suggested. All methods are compared, using two different datasets and several criteria regarding their ability to assess the evidential value of RNA profiles. We conclude that both the naïve Bayes' method and a method based on multinomial logistic regression, that produces a probabilistic statement as measure of the evidential value, are an important improvement of the existing methods. Besides a better performance, they are flexible and can be adapted to other situations. For example, they could potentially assist in the combination of RNA with DNA evidence., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model different evidential structures that can occur when linking crimes, and how they assist in understanding the complex underlying dependencies. That is, how evidence that is obtained in one case can be used in another and vice versa. The flip side of this is that the intuitive decision to "unlink" a case in which exculpatory evidence is obtained leads to serious overestimation of the strength of the remaining cases., (Copyright © 2014 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.)