This project has researched the consequences of blockchain technology on the expected future role of banks. And furthermore, how banks can respond to these effects to improve the innovation strategy and remain their future relevance.

The use of machine learning systems has great potential to better predict probabilities of default for credit underwriting. Despite this advantage, herewith there exists the substantial risk of discrimination. Moreover, machine learning models with the highest prediction-accuracy are often the least explicable (i.e. explainable). Nonetheless, explicability is needed to create accountability of automated credit decisions by machine learning systems. Furthermore, there exists a regulatory need for explicability of machine learning systems in the General Data Protection Regulation (GDPR) and the Consumer Credit Directive (CCD). Besides that, an ethical- and societal need exists for explicability. Within the exploration of literature, it becomes clear that research lacks on how to move from a high-level principle like explicability, towards a prospective assessment of a machine learning use case on this principle, it lacks a multi-disciplinary perspective, and it misses an assessment framework that can guide decision-makers within machine learning use cases, aligned with a multi-organizational development lifecycle. This research aims to design a prospective pragmatic assessment framework that can guide decision-makers, within machine learning applications in European credit underwriting cases from the point of view of explicability. To accomplish this, the Design Science Research Methodology (DSRM), complemented with the Value Sensitive Design (VSD) approach, is utilized. To this end, the Explicability Assessment Framework (EAF) was developed. This framework is adapted to the context- and explanation characteristics of the case, and aligns with the CRISP-DM development lifecycle. It was found in two case studies that the framework helps with the decision-making whether a machine learning system is sufficiently explicable or not. Lastly, a wide range of future research areas is identified that needs attention: empirical validation and expansion of the framework, the relevance for automated explanation creation, the scalability to other context and a large amount of explanations, and the practical perspective regarding adoption in the industry.

Difficulties arise in the making of predictions, since ideas and views on the future are often based on current conditions and, with only minor adjustments, it is simply expected that those conditions will continue into the future. With the goal of supporting experts in their scenario analysis this thesis is part of a pilot between the analysis network on national security (Analistennetwerk Nationale Veiligheid) of the Netherlands and the Technical University Delft. It explores if system dynamics is an useful approach in the modelling and simulation of changes in state power dynamics. It does so by researching literature on national power and through the analysis multi-scale geopolitical model to simulate over 217 countries. Exploratory modelling and analysis has been performed on the power dynamics of The United States, China, and Russia. This thesis concludes that system dynamics is an appropriate approach in the modelling and simulation of geopolitics and shifts in power dynamics. But, that uncertainties in the relationships between variables, missing data, and the modelling choices made to overcome these issues make it very difficult to built a good causal model to support expert scenario analysis.

The Netherlands is becoming increasingly diverse, amongst other things, due to ethnicity, religion, and urbanity. These diverse societies cluster in districts all across the Netherlands. There is an increase in tension between social groups, especially between people with and without migration background. Data analysis is used to measure the public opinion of each district in the Netherlands based on election results. System dynamics is used to model the public opinion over time and the polarizing behavior within districts. Polarization is a thought construct and does not need physical evidence to occur so the public debate plays a large role because it creates fuel for polarization. Even though there are national trends regarding public opinion, it is advised to look at a district in specific to understand why polarization may occur there.

As blockchain technology became more established in the financial arena, federal health IT officials, vendors and developers started looking at its potential and its use in the healthcare sector. Lately, there has been a major hype around the potential of the technology. It is described as a disruptive technology that could enable business model innovation with increased transparency, new models of partnerships and possible disintermediation. Business modelling was recognized as a potential tool to help the healthcare sector to determine a technology implementation strategy by involving all the important stakeholders in a value-driven dialogue about the way in which the technology should be utilized. This research focused on exploring the potential of the blockchain technology for business model innovation with the use of business model patterns as a theoretical construct. From the scientific perspective, research contributed to a better understanding of the role of business model patterns as a construct with multipurpose character. The construct was used in a role of classification device to gain an understanding over the technology associated business model logics and in a role of instruments of scientific inquiry to explore potential technology implications. A two-way relationship between the disruptive technology and business models was considered. As a result, tokenization was proposed as a new business model pattern, specific to the blockchain technology. Moreover, value patterns (patient empowerment, privacy by design, security by design, transparency by design) were recognized as an important matter that is potentially impacting future business models. In the second step, business model patterns were evaluated in the context of e-Health industry specific moderators. A Discussion with the implication of the technology from the business model innovation perspective was provided.

This study approached migration-related problems from a global perspective. The inter- connected nature of migration means that migration problems accumulate and intensify throughout the world. To capture these effects, a data-rich model component was devel- oped for global migration dynamics. To that extent, this study has developed the following innovations on a methodological level: a technique to comprehensively implement spatial phenomena into System Dynam- ics, a method to implement multi-scale System Dynamics through subscript mapping, and a semi-automated data acquisition process to obtain and structure a great variety of data sets on a country level. The nature of the developed model component is generic. That is, the component is scalable, and resolution-independent, as it is initialized through exter- nal databases. Therefore, a significant part of this study comprised the development of a semiautomated data acquisition process. The model component was then connected to a root-causes model component to analyze future migration scenarios on a global scale. It was found that migration is expected to increase in line with population growth. Policies were implemented and tested on a global, regional, and national scale. In the global policy case, the location of additional shelter and coping capabilities was found to greatly influence migration dynamics across the world. Additional shelters in low-income countries greatly reduced the number of migrants traveling towards the more wealthy coun- tries. The regional policy—closure of the European borders when a certain threshold is reached—had significant effects on the countries near the border, but failed to significantly improve the situation in the rest of Europe. This can be attributed to the delayed effects of such policies in these countries. National policies were tested for the Netherlands. Effects of these policies were insignificant, due to the inertia of the migration system. The number of migrants in Germany—a neighboring country—did not significantly change, and even the effect on the number of migrants in the Netherlands was negligible. Therefore, the first advice from this study towards policy-makers is that it might be more effective to address the root causes of migration, rather than just mitigating its detrimental effects. In addition, the inertia of the migration system requires globally aligned, large-scale policy implementation to prevent and mitigate migration-related problems. The delay in these policies should not be neglected, so it is important to identify potential problems as early as possible and take appropriate action.

There is still much debate in the safety literature about how to deal with the complexi- ties and dynamics of large-scale socio-technological systems especially. How, for in- stance, comes safety about in ‘high-tech’ complex systems? Also, how should assess- ments of technological (or artefactual) safety be performed in these kinds of systems? This thesis attempts to provide some practical applications and guidelines for these kinds of assessments, in real-life settings specifically. To this, four empirical studies of several high-tech military systems are discussed, and then how these studies were used for gaining a more adequate understanding of the dynamics of technological (or artefactual) safety in large-scale socio-technological systems in general. The thesis dis- cusses furthermore a study after the concept of responsible innovation, which is a field that closely relates to technological safety. This particular study points out that what was found for the assessment of technological safety could also be helpful for related allocations of responsibility in the systems studied here. An important factor appears to be in this all, that in practice the social and the technolo- gical (or artefactual) domain are linked in these kinds of systems to an extent that is not often acknowledged. Assessments of technological or artefactual safety in large-scale socio-technological systems, as well as allocations of responsibility in these kinds of systems, require, therefore, an empirical analysis of the socio-technological dynamics involved. These assessments and moral judgments should attempt, in short, to make sense of the empirical complexities in real-life settings. Important in this is that they should take into account – but also account for – the interactions between the tech- nologies (or artefacts) studied and the social structure(s) that these technologies (or artefacts) are embedded within.@en

In the past decade, rapid shifting and evolving technological systems that take trust as part of the design objective (call such systems “trust-inviting systems”) have incredibly transformed the way we interact with others. Think of reputation-based platforms mediating interactions between strangers and other digital services provided by institutions, as well as blockchain systems, which work to shape, respectively, our trust relations with individuals, institutions, and technologies. However, the ways these systems use to facilitate trust are not always justifiable and can lead to negative moral and social consequences if certain parts of the systems are shown to be flawed. By reflecting on several present cases of trust-inviting systems that are experiencing great tension of trust, this thesis argues that trust-inviting systems essentially attempt to interpret, translate, and ultimately institutionalize the idea of trustworthiness in given contexts. However, the ways that trust-inviting systems are using to institutionalize the characteristics of trustworthy persons, institutions, and technologies should not be accepted without scrutiny. For each case analysed here, a discrepancy between the intention to improve trust and trustworthiness and the means that are adopted to facilitate them is shown. Such cleavage is argued to be primarily caused by flawed understanding of the trust concepts and the resulting ill-suited design choices, as well as problems emerged from the implementation process. These issues are proposed to be ameliorated by a recalibration of the understanding of the trust concepts, which has the potential for remedying shortcomings of the current design and development of the systems with forward-looking strategies taking into account a wide range of societal needs, values, and technical properties. In a word, it is argued that trust-inviting digital systems should be designed, developed, and deployed in ways that are aligned with the essence of the trust relation in context, in order to achieve proper trust and trustworthy systems. As such, the pitfalls identified in each case are able to be used as perspectives contributing to building affordances that foster warranted trust and foreclosing affordances that would undermine warranted trust. @en

In response to the pressing demand for elderly care, care robots, the robots used by care receivers and/or caregivers for care purposes in various settings, such as hospitals, nursing homes, and personal residences were introduced and have been gaining traction as a technological solution to improve care quality and to enhance the value of autonomy. Despite all the benefits offered by robotic innovations, the consequent ethical issues in human-robot relationships in elderly care warrant sustained scrutiny. Due to the technological breakthroughs in robotics and its impacts on relationships in elderly care, the conventional dyadic human-robot interaction (HRI) model which focuses on one human and one robot, and the dominant Western individualistic understanding of autonomy are insufficient for the ethical evaluation of robots in elderly care.@en