ABSTRACT Assessment of the risk of engaging in a violent radicalization/extremism trajectory has evolved quickly in the last 10 years. Guided by what has been achieved in psychology and criminology, scholars from the field of preventing violent extremism (PVE) have tried to import key lessons from violence risk assessment and management, while bearing in mind the idiosyncrasies of their particular field. However, risk tools that have been developed in the PVE space are relatively recent, and questions remain as to their level of psychometric validation. Namely, do these tools consistently and accurately assess risk of violent extremist acting out? To answer this question, we systematically reviewed evidence on the reliability and validity of violent extremism risk tools. The main objective of this review was to gather, critically appraise, and synthesize evidence regarding the appropriateness and utility of such tools, as validated with specific populations and contexts. Searches covered studies published up to December 31, 2021. They were performed in English and German across 17 databases, 45 repositories, Google, other literature reviews on violent extremism risk assessment, and references of included studies. Studies in all languages were eligible for inclusion in the review. We included studies with primary data resulting from the quantitative examination of the reliability and validity of tools used to assess the risk of violent extremism. Only tools usable by practitioners and intended to assess an individual's risk were eligible. We did not impose any restrictions on study design, type, method, or population. We followed standard methodological procedures outlined by the Campbell Collaboration for data extraction and analysis. Risk of bias was assessed using a modified version of the COSMIN checklist, and data were synthesized through meta‐analysis when possible. Otherwise, narrative synthesis was used to aggregate the results. Among the 10,859 records found, 19 manuscripts comprising 20 eligible studies were included in the review. These studies focused on the Terrorist Radicalization Assessment Protocol (TRAP‐18), the Extremism Risk Guidance Factors (ERG22+), the Multi‐Level Guidelines (MLG‐V2), the Identifying Vulnerable People guidance (IVP guidance), and the Violent Extremism Risk Assessment (VERA)—all structured professional judgment tools—as well as Der Screener—Islamismus, an actuarial scale. Studies mostly involved adult male participants susceptible to violent extremism (N = 1106; M = 58.21; SD = 55.14). The types of extremist ideologies endorsed by participants varied, and the same was true for ethnicity and country/continent of provenance. Encouraging results were found concerning the inter‐rater agreement of scales in research contexts (kappas between 0.76 and 0.93), but one of the two studies that examined it in a field setting obtained disappointing results (kappas ranging between of 0.47 and 0.80). Content validity studies indicated that PVE risk tools adequately cover the risk factors and offending processes of individuals who go on to commit extremist violence. Construct validity analyses were few and far between, with results indicating that empirical divisions of scales did not match their conceptual divisions. The internal consistency of subscales was lackluster (Cronbach's alphas between 0.19 and 0.85), whereas full scales demonstrated acceptable internal consistency when assessed (0.80 for the ERG22+ and 0.64 for the IVP guidance). Only one study examined convergent validity, and it revealed a lack of convergence, primarily due to particularities of the scale under study (the MLG‐V2). Discriminant validity analyses were exploratory in nature, but suggested that PVE risk tools might not be ideology‐specific and may apply to both group and lone actors. Finally, although the TRAP‐18 showed a relatively strong postdictive effect size (pooled r = 0.62 [0.35–0.77], p = 0.000), the results were highly heterogeneous (I2 = 86%), and all studies used retrospective designs, meaning the outcome was already known at the time of assessment. As such, no included study evaluated true predictive validity (i.e., the ability to forecast future violent extremist outcomes based on prospective risk assessment). This represents a significant evidence gap. Threats to validity were substantial: (a) Many studies were case studies or had very small samples, (b) nearly all samples were constituted through the triangulation of publicly available data, and (c) convenience outcome measures were often used. Although having imperfect data is better than having no data, the current state of empirical validation precludes the recommendation of one tool over another for specific populations and contexts, and calls for higher‐quality validation studies for PVE risk assessment tools. Nevertheless, these tools constitute useful checklists of relevant risk and protective factors that could be taken into account by evaluators who wish to assess the risk of violent extremism and identify intervention targets.

Individualization of head-related transfer functions (HRTFs) can improve the quality of binaural applications with respect to the localization accuracy, coloration, and other aspects. Using anthropometric features (AFs) of the head, neck, and pinna for individualization is a promising approach to avoid elaborate acoustic measurements or numerical simulations. Previous studies on HRTF individualization analyzed the link between AFs and technical HRTF features. However, the perceptual relevance of specific errors might not always be clear. Hence, the effects of AFs on perceived perceptual qualities with respect to the overall difference, coloration, and localization error are directly explored. To this end, a listening test was conducted in which subjects rated differences between their own HRTF and a set of nonindividual HRTFs. Based on these data, a machine learning model was developed to predict the perceived differences using ratios of a subject's individual AFs and those of presented nonindividual AFs. Results show that perceived differences can be predicted well and the HRTFs recommended by the models provide a clear improvement over generic or randomly selected HRTFs. In addition, the most relevant AFs for the prediction of each type of error were determined. The developed models are available under a free cultural license.

The individualization of head related transfer functions (HRTFs) can make an important contribution to improving the quality of binaural technology applications. One approach to individualization is to exploit relations between the shape of HRTFs on the one hand and anthropometric features of the ears, head, and torso of the corresponding listeners on the other hand. To identify statistically significant relations between the two sets of variables, a relatively large database is required. For this purpose, full-spherical HRTFs of 96 subjects were acoustically measured and numerically simulated. A detailed cross-evaluation showed a good agreement to previous data between repeated measurements and between measured and simulated data. In addition to 96 HRTFs, the database includes high resolution head-meshes, a list of 25 anthropometric features per subject, and headphone transfer functions for two headphone models.

Numerical simulations offer a feasible alternative to the direct acoustic measurement of individual head-related transfer functions (HRTFs). For the acquisition of high quality 3D surface scans, as required for these simulations, several approaches exist. In this paper, we systematically analyze the variations between different approaches and evaluate the influence of the accuracy of 3D scans on the resulting simulated HRTFs. To assess this effect, HRTFs were numerically simulated based on 3D scans of the head and pinna of the FABIAN dummy head generated with 6 different methods. These HRTFs were analyzed in terms of interaural time difference, interaural level difference, energetic error in auditory filters and by their modeled localization performance. From the results, it is found that a geometric precision of about 1 mm is needed to maintain accurate localization cues, while a precision of about 4 mm is sufficient to maintain the overall spectral shape.

The paper connects two interrelated discourses: criticality assessments and cascading effects. During crises, crisis managers have to constantly assess and reassess the criticality of systems and elements in order to identify potential triggers for cascading effects and to distribute efficiently available resources for mitigation. To help practitioners to make the right decisions, models are needed for the preparation phase that extend their knowledge about dependency relations and critical system elements. To do so, the paper proposes a concept of dynamic interdependencies and criticality. It is argued that dependency relations and their impact on system elements are changing over time. We maintain that when elements and system either fail or become involved, or when resources become scarce and then regain availability, then the criticality of the individual element, the system, the resource, and finally the overall situation changes as well, either positively or negatively. This article also presents a software tool which models the dynamics of cascading crisis scenarios. Finally, this software is used to reconstruct an example of a cascading power failure to demonstrate how criticality evolves dynamically.

This article considers the challenges of policing terrorism in social media and investigates whether and how these challenges are being addressed in the research and development of tools to detect radicalisation in social media. The availability of big data tools for the analysis of social media has raised concerns about the onset of algorithm-driven profiling techniques leading to social control that extends to large populations. However, it has not been clarified whether such tools are used by the police and whether they fit the requirements of policing in the field of terrorism. The article provides an overview on research and development of big data tools using data from social media in the field of terrorism and extremism. The tools presented predominantly use supervised machine learning classifiers to discriminate between two classes of content, e.g. radical and non-radical. Most of the tools follow a technology-driven approach aiming to optimise the precision of algorithms. They lack both a conceptual model of (violent) radicalisation as well as a clear focus on providing decision support in terms of helping human analysts to filter data. The article argues that technology-driven approaches do not fit with current practices of policing in the field of terrorism and extremism, which build on professional judgement rather than algorithm-driven pattern identification in big data. The paper concludes with the hypothesis that the application of machine learning algorithms can support analysts in generating predictive knowledge, but will not lead to an algorithm-driven security production.

In current discussions about prevention and legal administration of police use of force it seems that their legal control is predominantly a problem of penal investigations and sanctions. When focusing on the illegitimate use of force by the police it does not take into account that their behavior is often experienced as illegitimate by the involved party whether it was illegitimate or not. Therefore we argue that in order to control police use of force it is necessary to have legal forms of control which applies to the escalating dynamics in protest policing and takes the perception of legitimacy into account.. This article is based on interviews with participants and bystanders of the May Day protests in Berlin Kreuzberg 2009. Suggesting how to include the aspects of legitimacy in order to control and prevent police brutality.