To combat the effects of climate change, there is a worldwide shift to reduced emissions and increased use of renewable energy sources. Solar energy is a vital part of this transition and necessary to be able to achieve the targets. The rapid pace of adaptation raises questions about the negative equity and justice associated with this development. Along with the rapid growth, regional differences in solar panel adoption have been found across the world. Besides, government incentives and policies have played an important role in both adoption growth and justice implications.

This study explores the pace and equitability of the solar energy transition in Amsterdam, the Netherlands. The study evaluates the potential for solar energy, the disparity of adoption, what socio-economic factors are correlated to adoption patterns, and how adoption might evolve in the future and under different policy measures. To do so, a structured, integrated and data-driven approach is designed to meet the research objectives, and possibly serve as a policy tool for future studies.

The study shows that there is an "adoption gap" in Amsterdam, meaning that solar panels and the benefits of incentives end up with a specific group of citizens. Therefore, targeted policy measures are necessary to ensure an equitable transition.

Currently, the electricity grid in the Netherlands is reaching its capacity, resulting in congestion issues. One of the factors that causes the electricity grid to become overloaded is the increasing use of EVs (electric vehicles). A situation in which a large number of EV users in a certain area charge their EV at the same time can significantly increase the risk of congestion in the electricity network. To avoid such situations it is necessary to change the charging behaviour of the EV users.

The literature shows that smart charging systems, such as charging schemes, are having a high potential to solve these grid capacity problems. In the context of this study, a charging scheme is defined as a contract between an EV user and the charge card provider/electricity supplier stating, among others, at what times an EV user could charge his/her EV. However, to ensure that charging schemes can effectively contribute to solving the grid capacity problems, it is important that many EV users are willing to participate in such schemes.

Therefore, the research objective is to study how EV users will react to various charging schemes. In particular, the aim is to assess the effectiveness of appealing to someone's intrinsic motivation versus providing extrinsic incentives. First of all, with regard to the extrinsic incentives, this study specifically concerns monetary incentives. By presenting different discounts, it is possible to test whether a monetary compensation influences the willingness to participate in a charging scheme. Secondly, with regard to someone’s intrinsic motivation, this study focuses on appealing to someone's solidarity as a form of an intrinsic motivation. EV users with a certain degree of solidarity can be motivated to participate in a charging scheme when solidarity incentives are obtained (social recognition, togetherness and the development of friendships). By emphasizing the social benefits of a charging scheme, solidarity incentives could possibly increase the willingness to participate in a charging scheme.

Since the objective of the study is to research how EV users will react to various charging schemes, a survey containing a discrete choice experiment is used. To be able to examine the effectiveness of providing an extrinsic incentive (monetary compensation) versus appealing to someone's solidarity by emphasizing the social benefits of a charging scheme, the survey contains two charging scheme versions, an incentive-based version and an intrinsic-based version. By using two survey versions, it is possible to examine separately whether it is more effective to appeal to someone's intrinsic motivation versus providing a financial compensation.

The results show that the willingness to participate in a charging scheme was generally higher in the intrinsic-based version than in the incentive-based version. Based on this, it can be concluded, within the boundaries of the selected attribute ranges, that appealing to someone's solidarity by emphasizing the social benefits is more effective than offering a financial compensation. This is also substantiated by the fact that the financial compensation attribute included in the incentive-based charging schemes does not influence the willingness to participate.

The use of Automated Decision Making (ADM) systems in the public sector will become increasingly prevalent in the future, making citizens increasingly likely to be confronted with decisions that have been made fully automatically, without human intervention. Ever more digitization increases the opaqueness of the social benefits system in the Netherlands and leads to citizens facing most of the adverse, sometimes unforeseen effects of automation. Why and how unsafeness and unexpected effects emerge is not well known. In this research, these effects, defined to be ripple effects, are explored using a system safety lens and proposing an agent-based model design to explore why and how the effects emerge.

The leading cause of ripple effects was found to be the lack of a hierarchical safety structure, seen in laws being organized per organization, and the lack of an overarching controller to ensure that overlap does not occur.
This is partly due to a lack of an accurate model representing a Dutch citizen. Several policy recommendations are presented, including a rudimentary set-up on how to apply a safety control structure within the social benefits system.

Governmental AI-supported decision-making is paramount when impacting citizens. Citizens are subject to their government’s decision-making, which is crucial when they transact, as examining the transaction’s rightfulness is executed by the same government. Thus, control and monitoring are evident, which are increasingly applied through AI-supported tools (Hoekstra et al., 2021). It is deemed socially unacceptable when government falsely accuses their citizens of unrightful transactions. Vice versa, it is also deemed unacceptable when criminals taking advantage of public money are exonerated. The citizens whom are trapped in these systems, like the benefits system, are often vulnerable and have low incomes. The Dutch government wrongly accused 20.000 parents of fraud, resulting in major con-
sequences. Therefore, research is crucial for both a better functioning government and protecting the safety of vulnerable citizens. Scientific relevance is emphasised by the narrow understanding of how harm of next similar case is prevented, as such systems are barely researched holistically. Limited
understanding of the relations within and between socio-technological contexts and the safekeeping of citizens lead to the main question:

“How can citizen’s safety be safeguarded in governmental AI-supported decision-making?”

Design science is the main methodology, consisting of three cycles: the rigour cycle, relevance cycle, and design cycle (Hevner, 2014). The method allows for innovative thinking and combining empirical (relevance) and scientific (rigour) knowledge toward exploring a new solution, in this research, safeguarding citizen’s safety (design). Additionally, it allows for a system’s approach required to understand the relationships of different decision-making components and for combining empirical and scientific insights (vom Brocke et al., 2020). Combining this with a holistic perspective, the research incorporates strategical, tactical, and operational challenges and specifically including the political dimension; more than factors and actors. Insight is created between the different context, which are all crucial in establishing a well balanced system.
In seeking an answer to the main question, three parts are crucial: the system of AI-supported decision-making, the concept of citizen’s safety and how both can be integrated. The system and its boundaries are discovered through scientific and empirical exploration, serving as a framework. The definition of citizen’s safety and its implications are discovered by relating to the system and its characteristics. With these two parts, the influence of the system on citizen’s safety is explored and leads to the third crucial part, validated by a serious game. All parts use different sub-methodologies to obtain
the required knowledge for the design science methodology.

Explainable AI is the field concerned with trying to make AI understandable to humans. While efforts have resulted in significant improvement in research and practical methods of Explainable AI, there is an urgent need for additional research and empirical studies. The academic research gaps identified in this thesis show that Explainable AI is still in its infancy and is mostly approached with a technocentric perspective while not being focused on the audience the explainability is actually intended for. Next to this, there is no structured approach to defining and establishing explainability in dynamic complex systems that involve people, institutional, and organizational elements. Lastly, there are limited empirical studies that investigate the needs, usage, and risk of explainability in complex systems.

The research tries to define and address explainability in the socio-technical context within the Machine Learning decision support systems of Transaction Monitoring. It does so by performing an extensive literature review and by conducting semi-structured that try to collect empirical knowledge within local practice. The goal of this research is to expand the definitions and view on explainability by incorporating the social, organizational, and institutional elements that influence explainability. Next, the goal is to develop a method that can help practitioners approach explainability in a structured manner taking the audience into account while applying this socio-technical perspective.

The user-centered method for operationalizing explainability takes on a socio-technical perspective and can provide requirements for design choices, in addition to this the method shows how these requirements can be satisfied and controlled by instantiating control structures. The method has been demonstrated and evaluated within the bank by providing a workshop using a Toy Case during a focus group. Practitioners experienced the method as useful and actionable, also the method provides broader perspectives and insights on explainability and invites the discussion of dilemmas and questions. The practitioners added that the method could be further refined by focusing on additional guidance on the control structure because the method assumes prerequisite knowledge of systems theory and system safety theory.

To match supply from intermittent renewable energy sources (RES) with demand, it is proposed in literature to introduce flexibility in the electricity market of the future. Flexibility can be provided by energy storage, demand response and cross-border transmission. In this thesis flexibility is modeled explicitly through the price mechanism of demand and supply.
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.

As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future.

There is a need for a more comprehensive sociotechnical systems view on ML. Such a view looks at the development and use of an ML system in practice as being a sociotechnical ML system: "a system consisting of technical artefacts, human agents and institutions, in which a machine-based subsystem influences its real or virtual environment by automating, supporting or augmenting decision-making". This research takes on this view to design a sociotechnical guide for ML practice, centring the specication of sociotechnical ML systems. Taking on the guidelines contributes to a safe and effective development and use of ML systems.

Scandals in which governmental ADM tools played a role, have recently brought
about political and societal debate about the potential harms to citizens that such automated systems potentially bring. This thesis focuses on ADM systems which contain an AI component. Based on the knowledge gaps perceived,
the main research question for this study is: In public sector AI systems, what are emerging vulnerabilities for citizens and how do these translate into
governance requirements for decision-makers? The approach to answer this question is an adjusted form of Theory Building from Case Study. A layered ’onion model’ presents four relevant contexts to consider for AI System vulnerabilities specifically in the public sector: AI model, Model deployment, Political-administrative, and Societal. The case study results demonstrate that dealing with vulnerabilities in one of the four model contexts often complicates dealing with vulnerabilities in the other contexts. Hence, the vulnerabilities model points to so-called governance requirements dilemmas.