From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation.

Techniques from artificial intelligence (AI) can be used in forensic evidence evaluation and are currently applied in biometric fields. However, it is generally not possible to fully understand how and why these algorithms reach their conclusions. Whether and how we should include such 'black box' algorithms in this crucial part of the criminal law system is an open question that has not only scientific but also ethical, legal, and philosophical angles.

Fusing linguistic and acoustic information for automated forensic speaker comparison.

Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect. The question can be addressed given disputed and reference speech material, adopting the recommended and scientifically accepted likelihood ratio framework for reporting evidential strength in court. In forensic practice, usually, auditory and acoustic analyses are performed to carry out such a verification task considering a diversity of features, such as language competence, pronunciation, or other linguistic features.

An overview of log likelihood ratio cost in forensic science - Where is it used and what values can we expect?

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 () is a popular metric for such systems, penalizing misleading LRs further from 1 more.  = 0 indicates perfection while  = 1 indicates an uninformative system.

From data to a validated score-based LR system: A practitioner's guide.

Likelihood ratios (LRs) are a useful measure of evidential strength. In forensic casework consisting of a flow of cases with essentially the same question and the same analysis method, it is feasible to construct an 'LR system', that is, an automated procedure that has the observations as input and an LR as output. This paper is aimed at practitioners interested in building their own LR systems.

Development and validation of a fast and automated DNA identification line.

The importance of DNA evidence for gaining investigative leads demands a fast workflow for forensic DNA profiling performed in large volumes. Therefore, we developed software solutions for automated DNA profile analysis, contamination check, major donor inference, DNA database (DDB) comparison and reporting of the conclusions. This represents the Fast DNA IDentification Line (FIDL) and this study describes its development, validation and implementation in criminal casework at the authors' institute.

A strawman with machine learning for a brain: A response to Biedermann (2022) the strange persistence of (source) "identification" claims in forensic literature.

We agree wholeheartedly with Biedermann (2022) FSI Synergy article 100222 in its criticism of research publications that treat forensic inference in source attribution as an "identification" or "individualization" task. We disagree, however, with its criticism of the use of machine learning for forensic inference. The argument it makes is a strawman argument.

Objectifying evidence evaluation for gunshot residue comparisons using machine learning on criminal case data.

Comparative gunshot residue analysis addresses relevant forensic questions such as 'did suspect X fire shot Y?'. More formally, it weighs the evidence for hypotheses of the form H: gunshot residue particles found on suspect's hands are from the same source as the gunshot residue particles found on the crime scene and H: two sets of particles are from different sources. Currently, experts perform this analysis by evaluating the elemental composition of the particles using their knowledge and experience.

Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles.

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable artificial intelligence (XAI) to help users understand why specific predictions are made.

Consensus on validation of forensic voice comparison.

Since the 1960s, there have been calls for forensic voice comparison to be empirically validated under casework conditions. Since around 2000, there have been an increasing number of researchers and practitioners who conduct forensic-voice-comparison research and casework within the likelihood-ratio framework. In recent years, this community of researchers and practitioners has made substantial progress toward validation under casework conditions becoming a standard part of practice: Procedures for conducting validation have been developed, along with graphics and metrics for representing the results, and an increasing number of papers are being published that include empirical validation of forensic-voice-comparison systems under conditions reflecting casework conditions.

Why calibrating LR-systems is best practice. A reaction to "The evaluation of evidence for microspectrophotometry data using functional data analysis", in FSI 305.

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.

Establishing phone-pair co-usage by comparing mobility patterns.

In forensic investigations it is often of value to establish whether two phones were used by the same person during a given time period. We present a method that uses time and location of cell tower registrations of mobile phones to assess the strength of evidence that any pair of phones were used by the same person. The method is transparent as it uses logistic regression to discriminate between the hypotheses of same and different user, and a standard kernel density estimation to quantify the weight of evidence in terms of a likelihood ratio.

Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach.

The number of contributors (NOC) to (complex) autosomal STR profiles cannot be determined with absolute certainty due to complicating factors such as allele sharing and allelic drop-out. The precision of NOC estimations can be improved by increasing the number of (highly polymorphic) markers, the use of massively parallel sequencing instead of capillary electrophoresis, and/or using more profile information than only the allele counts. In this study, we focussed on machine learning approaches in order to make maximum use of the profile information.

Transmission tree of the highly pathogenic avian influenza (H5N1) epidemic in Israel, 2015.

The transmission tree of the Israeli 2015 epidemic of highly pathogenic avian influenza (H5N1) was modelled by combining the spatio-temporal distribution of the outbreaks and the genetic distance between virus isolates. The most likely successions of transmission events were determined and transmission parameters were estimated. It was found that the median infectious pressure exerted at 1 km was 1.

Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse.

Anatomical tract tracing methods are the gold standard for estimating the weight of axonal connectivity between a pair of pre-defined brain regions. Large studies, comprising hundreds of experiments, have become feasible by automated methods. However, this comes at the cost of positive-mean noise making it difficult to detect weak connections, which are of particular interest as recent high resolution tract-tracing studies of the macaque have identified many more weak connections, adding up to greater connection density of cortical networks, than previously recognized.

Default Mode Hypoconnectivity Underlies a Sex-Related Autism Spectrum.

Background: Females and males differ significantly in the prevalence and presentation of autism spectrum conditions. One theory of this effect postulates that autistic traits lie on a sex-related continuum in the general population, and autism represents the extreme male end of this spectrum. This theory predicts that any feature of autism in males should 1) be present in autistic females, 2) differentiate between the sexes in the typical population, and 3) correlate with autistic traits.

Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery.

Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory.

Wiring cost and topological participation of the mouse brain connectome.

Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of "wiring cost" explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain.

Effect of hyperbaric oxygen conditions on the ordering of interfacial water.

Hyperbaric oxygen (HBO2) conditions are applied clinically to treat diverse conditions. There is a lack of a unifying consensus as to how HBO2 acts effectively against a broad range of medical conditions, and numerous differing biological explanations have been offered. The possibility of a mechanism dependent on the extensive ordering of interfacial water has not yet been investigated.

OutbreakTools: a new platform for disease outbreak analysis using the R software.

The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R.

Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available.

Viral substitution rate variation can arise from the interplay between within-host and epidemiological dynamics.

The evolutionary rates of RNA viruses can differ from one another by several orders of magnitude. Much of this variation has been explained by differences in viral mutation rates and selective environments. However, substitution rates also vary considerably across viral populations belonging to the same species.

Finding evidence for local transmission of contagious disease in molecular epidemiological datasets.

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection.

Infectious disease burden related to child day care in the Netherlands.

Background: Studying day-care-associated infectious disease dynamics aids in formulating evidence-based guidelines for disease control, thereby supporting day-care centers in their continuous efforts to provide their child population with a safe and hygienic environment. The objective of this study was to estimate the (excess) infectious disease burden related to child day-care attendance in the Netherlands.

Methods: A Dutch surveillance network of child day-care centers (DCCs) prospectively reported on infectious disease episodes and related use of health care among their child population on a daily basis from March 2010 to March 2012.

A sign of superspreading in tuberculosis: highly skewed distribution of genotypic cluster sizes.

Background: Molecular typing is a valuable tool for gaining insight into spread of Mycobacterium tuberculosis. Typing allows for clustering of cases whose isolates share an identical genotype, revealing epidemiologic relatedness. Observed distributions of genotypic cluster sizes of tuberculosis (TB) are highly skewed.

Genetic data provide evidence for wind-mediated transmission of highly pathogenic avian influenza.

Outbreaks of highly pathogenic avian influenza in poultry can cause severe economic damage and represent a public health threat. Development of efficient containment measures requires an understanding of how these influenza viruses are transmitted between farms. However, the actual mechanisms of interfarm transmission are largely unknown.