A response to “Likelihood ratio as weight of evidence: a closer look” by Lund and Iyer

Recently, Lund and Iyer (L&I) raised an argument regarding the use of likelihood ratios in court. In our view, their argument is based on a lack of understanding of the paradigm. L&I argue that the decision maker should not accept the expert’s likelihood ratio without further consideration. This is agreed by all parties. In normal practice, there is often considerable and proper exploration in court of the basis for any probabilistic statement. We conclude that L&I argue against a practice that does not exist and which no one advocates. Further we conclude that the most informative summary of evidential weight is the likelihood ratio. We state that this is the summary that should be presented to a court in every scientific assessment of evidential weight with supporting information about how it was constructed and on what it was based

Identification and separation of DNA mixtures using peak area information (Updated version of Statistical Research Paper No. 25)

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed

Sensitivity analysis of a Bayesian network for reasoning about digital forensic evidence

Bayesian network representing an actual prosecuted case of illegal file sharing over a peer-to-peer network has been subjected to a systematic and rigorous sensitivity analysis. Our results demonstrate that such networks are usefully insensitive both to the occurrence of missing evidential traces and to the choice of conditional evidential probabilities. The importance of this finding for the investigation of digital forensic hypotheses is highlighted. © 2010 IEEE.published_or_final_versio

Evaluation of forensic DNA traces when propositions of interest relate to activities: analysis and discussion of recurrent concerns

When forensic scientists evaluate and report on the probative strength of single DNA traces, they commonly rely on only one number, expressing the rarity of the DNA profile in the population of interest. This is so because the focus is on propositions regarding the source of the recovered trace material, such as “the person of interest is the source of the crime stain.” In particular, when the alternative proposition is “an unknown person is the source of the crime stain,” one is directed to think about the rarity of the profile. However, in the era of DNA profiling technology capable of producing results from small quantities of trace material (i.e., non-visible staining) that is subject to easy and ubiquitous modes of transfer, the issue of source is becoming less central, to the point that it is often not contested. There is now a shift from the question “whose DNA is this?” to the question “how did it get there?” As a consequence, recipients of expert information are now very much in need of assistance with the evaluation of the meaning and probative strength of DNA profiling results when the competing propositions of interest refer to different activities. This need is widely demonstrated in day-to-day forensic practice and is also voiced in specialized literature. Yet many forensic scientists remain reluctant to assess their results given propositions that relate to different activities. Some scientists consider evaluations beyond the issue of source as being overly speculative, because of the lack of relevant data and knowledge regarding phenomena and mechanisms of transfer, persistence and background of DNA. Similarly, encouragements to deal with these activity issues, expressed in a recently released European guideline on evaluative reporting (Willis et al., 2015), which highlights the need for rethinking current practice, are sometimes viewed skeptically or are not considered feasible. In this discussion paper, we select and discuss recurrent skeptical views brought to our attention, as well as some of the alternative solutions that have been suggested. We will argue that the way forward is to address now, rather than later, the challenges associated with the evaluation of DNA results (from small quantities of trace material) in light of different activities to prevent them being misrepresented in court

Evaluation of forensic genetics findings given activity level propositions: A review.

The evaluation of results of forensic genetic analyses given activity level propositions is an emerging discipline in forensic genetics. Although it is a topic with a long history, it has never been considered to be such a critically important topic for the field, as today. With the increasing sensitivity of analysis techniques, and advances in data interpretation using probabilistic models ('probabilistic genotyping'), there is an increasing demand on forensic biologists to share specialised knowledge to help recipients of expert information address mode and timing of transfer and persistence of traces in court. Scientists thereby have a critical role in the assessment of their findings in the context of the case. This helps the judiciary with activity level inferences in a balanced, robust and transparent way, when based on (1) proper case assessment and interpretation respecting the hierarchy of propositions (supported by, for example, the use of Bayesian networks as graphical models), (2) use of appropriate data to inform probabilities, and (3) reporting guidelines by international bodies. This critical review of current literature shows that with certain prerequisites for training and quality assurance, there is a solid foundation for evidence interpretation when propositions of interest are at the 'activity level'

Evaluation of a prior-incorporated statistical model and established classifiers for externally visible characteristics prediction

Human identification through DNA has played an important role in forensic science and in the criminal justice system for decades. It is referring to the association of genetic data with a particular human being and has facilitated police investigations in cases such as the identification of suspected perpetrators from biological traces found at crime scenes, missing persons, or victims of mass disasters [1]. Currently there are two main methods developed: the genotyping through short tandem repeats (STR profiling) and the forensic DNA phenotyping (FDP). Despite the fact that these two methods are aiming in identifying a person through its genetic material, their approach and consequences that come up are completely different. STR profiling compares allele repeats at specific loci in DNA and aims at a match with already known to the police authorities DNA profiles, while FDP, which is the focus on the current study, aims in the prediction of appearance traits of an individual [2, 3]. In contrast with STR profiling, information that arise out of FDP cannot be used as sole evidence in the court [4]. The ability of predicting EVCs from DNA can be used as ‘biological witnesses’ that can only provide leads for the investigative authorities and subsequently narrow down a possible large set of potential suspects. The use of FDP begins a new era of ‘DNA intelligence’ and holds great promise especially in cases where individuals cannot be identified with the conventional method of STR profiling and also in cases where there is no additional knowledge on the sample donor. So far in FDP, traits such as eye, hair and skin color can be predicted reliably with high prediction accuracy and predictive models have already been forensically validated [5-7]. Regarding other appearance traits, the current lack of knowledge on the genetic markers responsible for their phenotypic variation and the lower predictability, especially of intermediate categories, has prevented FDP from being routinely implemented in the field of forensic science. The majority of the predictive models developed for appearance trait prediction were based on multinomial logistic regression (MLR) while only few used other methods such as decision trees and neural networks. Machine learning (ML) approaches have become a widely used tool for classification problems in several fields and they are known for their potential to boost model performance and their ability to handle different and complex types of data [8]. However, within the context of predicting EVCs, a systematic and comparative analysis among different ML approaches that could possibly indicate methods that outperform the standard MLR, has not been conducted so far. In addition, incorporation of priors in the EVC prediction models that may have potential to improve the already existing approaches, has not been investigated in the context of forensics yet. These priors indicate the trait category prevalence values among biogeographic ancestry groups, and their use would allow us to leverage Bayesian statistics in order to build more powerful prediction models. In our case, incorporation of such priors in the model could reflect the additional information from all yet unknown causal genetic factors and act as proxies in the prediction model. Therefore, those two approaches were conducted throughout my PhD project in order to improve the already existing approaches of FDP which was the main aim of my study. In the first study, I aimed to collect a comprehensive data set from previously published sources on the spatial distribution of different appearance traits. I conducted a literature review in order to assemble this information, which later on could be incorporated as priors in the EVCs prediction models. Due to the lack of available and reliable sources, our resulting data set contained only eye and hair color for mostly European countries. More specifically, I collected data on eye color from 16 European and Central Asian countries, while for hair color I collected data from seven European countries. For countries outside of Europe, where the variation is low, it was not possible to assemble trustworthy and population-representative data. Afterwards, I calculated the association of those two traits and obtained a moderate association between them. Interpolation techniques were applied in order to infer trait prevalence values in at least neighboring countries. Resulting prevalences and interpolated values were presented in spatial maps. The subject of the second study was to incorporate the trait prevalence values as priors in the prediction model. However, due to the lack of reliable data that was observed in the first study, the incorporation of the actual priors that would give us the actual insight of their impact in the EVC prediction was not feasible with the current existing knowledge and the available data. Therefore, I assessed the impact of priors across a grid that contained all possible values that priors can take, for a set of appearance traits including eye, hair, skin color, hair structure, and freckles. In this way, I aimed to assess potential pitfalls caused by misspecification of priors. Results were compared and evaluated with the corresponding prior-free' previously established prediction models. The effect of priors was demonstrated in the standard performance measurements, including area under curve (AUC) and overall accuracy. I found out that from all possible prior values, there is a proportion that shows potential in improving the prediction accuracy. However, possible misspecification of priors can significantly diminish the overall accuracy. Based on that, I emphasize the importance of accurate prior values in the prediction modelling in order to identify the actual impact. As a consequence of the above, the use of prior informed models in forensics is currently infeasible and more studies on the topic are necessary in order to extend the current knowledge on spatial trait prevalence. Finally, the focus of the third study was exploring and comparing the performances of methodologies beyond MLR. MLR is considered the standard method for predicting EVCs, since the majority of the predictive models developed are based on that method. Due to the fact that there is still potential for improvement of MLR models, especially for traits such as skin color or hair structure, I aimed at applying different ML methods in order to identify whether there is a potential classifier that outperforms the conventional method of MLR. Therefore I conducted a systematic comparison between MLR and three alternative ML classifiers, namely support vector machines (SVM), random forests (RF) and artificial neural networks (ANN). The traits that I focused on here were eye, hair, and skin color. All models were based on the genetic markers that were previously established in IrisPlex, HIrisPlex and HIrisPlex-S [5-7]. Overall, I observed that all four classifiers performed almost equally well, especially for eye color. Only non-substantial differences were obtained across the different traits and across trait categories. Given this outcome, none of the ML methods applied here performed better than MLR, at least for the three traits of eye, hair, and skin color. Ultimately, due to the easier interpretability of the MLR, it is suggested at least for now and for the currently known marker sets, that the use of MLR is the most appropriate method for predicting appearance traits from DNA. Throughout my PhD project, it became apparent that the available knowledge on spatial trait prevalence values was quite restricted not only in certain appearance traits but also in continental groups. More specifically, most available and reliable data were focused on European populations and the traits that were available were mostly for eye and hair color. For other traits, such as skin color, hair structure, and freckles, the data were either extremely few or nonexistent. This was a significant obstacle throughout the project, since it prevented me from applying and testing the actual impact of the accurate trait prevalence values as priors in EVC prediction. However, the lack of data presented an opportunity to perform in-depth theoretical research, in particular testing the impact of priors within a spatial grid that included its possible values. I found out that there is a proportion of priors that showed potential to improve EVC prediction. However, caution is advised regarding misspecification of priors that can significantly deteriorate the models' performance. Furthermore, the application of different ML approaches did not show any significant improvement on the prediction performance against the standard MLR. This could be due to the nature of the traits, since some of them are multifactorial and affected by various external independent factors or due to possible limitations of the currently known predictive markers. With the available knowledge so far, it is emphasized throughout this study that for the time being, priors are refrained from being incorporated in the EVC prediction models while from the different classifiers applied, MLR is considered as the most appropriate method for EVC prediction due to its easier interpretability. In addition, the presented study highlights the importance of reference data on externally visible traits and the identification of more genetic markers that contribute to certain traits and I hope that the present work will motivate the emergence of these certain types of data collections that potentially may improve the current EVC prediction models

A Taxonomy of Explainable Bayesian Networks

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made