This insightful book provides a unified repository of information on jihadist terrorism. Offering an integrated treatment of terrorist groups, zones of armed conflict and counter-terrorism responses from liberal democratic states, it presents fresh empirical perspectives on the origins and progression of conflict, and contemporary global measures to combat terrorist activity. @en

This insightful book provides an analysis of the central ethical issues that have arisen in combatting global terrorism and, in particular, jihadist terrorist groups, notably Al Qaeda, Islamic State and their affiliates. Chapters explore the theoretical problems that arise in relation to terrorism, such as the definition of terrorism and the concept of collective responsibility, and consider specific ethical issues in counter-terrorism. @en

Machine Learning (ML) is reaching the peak of a hype cycle. If you can think of a personal or grand societal challenge – then ML is being proposed to solve it. For example, ML is purported to be able to assist in the current global pandemic by predicting COVID-19 outbreaks and identifying carriers (see, e.g., Ardabili et al. 2020). ML can make our buildings and energy grids more efficient – helping to tackle climate change (see, e.g., Rolnick et al. 2019). ML is even used to tackle the very problem of ethics itself – creating an algorithm to solve ethical dilemmas. Humans, it is argued, are simply not smart enough to solve ethical dilemmas; however, ML can use its mass processing power to tell us the answers regarding how to be ‘good’, in the same way it is better at Chess or Go (Metz 2016). States have taken notice of this new power and are attempting to use ML to solve their problems, including their security problems and, of particular importance in this thesis, the problem of countering terrorism. Counterterrorism procedures including border checks, intelligence collection, waging war against terrorist armed forces, etc. These practices are all being ‘enhanced’ with ML-powered tools (Saunders et al. 2016; Kendrick 2019; Ganor 2019), including: bulk data collection and analysis, mass surveillance, and autonomous weapons among others. This is concerning. Not because the state should not be able to use such power to enhance the services it provides. Not because AI is in principle unethical to use – like land mines or chemical weapons. This is concerning because little has been worked out regarding how to use this tool in a way that is compatible with liberal democratic values. States are in the dark about what these tools can and should do. @en

The terrorist use of chemical, biological, radiological, and nuclear (CBRN) weapons is a worst-case scenario for most security agencies. Yet traditionally, the risk of CBRN-terrorism is characterized as a so-called “high impact – low probability” threat. Academics and analysts consider it challenging for terrorists to acquire these weapons and, hence, assign a low probability to the terrorist use of impactful CBRN weapons such as nuclear devices or weaponized microorganisms. Most researchers, however, assess the impact of a terrorist weapon solely based on its capability to physically destroy structures or harm organisms. This one-dimensional assessment rules out those toxic substances that are commonly considered CBRN-agents, but only possess limited destructive capabilities. Hence, these agents are not considered a priority for most security agencies. Rather, most resources in CBRN-defense are allocated to subjects like international non-proliferation efforts, whereas toxic substances that are openly available in hardware stores are often overlooked. The present study focuses on three of these toxic substances; ricin, phosphine, and americium. It will be shown that, while arguably having limited physical impact in the hands of terrorists, these and other toxic substances exhibit characteristics that could be of high value to the strategic and tactical goals of terrorist groups. For example, attacks with phosphine have the potential to inflict massive amounts of fear and disruption and are capable of causing political damage and damage to security institutions. The potential to inflict substantial amounts of non-kinetic damage as well as the availability and ease of use of these substances need to be properly acknowledged and met with a multi-layered web of countermeasures (web of prevention) by security institutions. However, as this thesis will show, this web of prevention ought to include not only Government agencies, but also other stakeholders such as manufacturers and vendors of these products, the press, researchers, and citizens. It will be argued that all of these stakeholder groups share a joint moral responsibility to combat terrorist attacks with toxic substances. This joint moral responsibility can be translated into specific actions of individuals in these groups of stakeholders that include, for example, the reporting of suspicious purchases in hardware stores or the flagging and deletion of weapon manuals on the internet. It will be shown that most of the current cooperative measures against these substances suffer from issues that can be traced back to the inability of the stakeholder groups to identify their respective responsibilities within the web of prevention. Furthermore, security institutions miss opportunities to operationalize the moral responsibilities of stakeholder groups such as vendors of toxic products. Based on this assessment, this thesis will give recommendations on how to improve the current CBRN security architecture. It will be shown that the responsibilities and actions of each stakeholder group have to be defined, discussed, and coordinated by all relevant stakeholder groups jointly. In order to do so, the theoretical concept of the web of prevention has to be turned into an institutionalized web in the form of a joint center. Such a joint center gives the stakeholder groups the opportunity to (1) assess the threat, (2) define tasks and actions of each group, and (3) equip each group with the means to perform these actions in an efficacious and ethically sustainable manner. @en