Artificial intelligence (AI) has the potential to revolutionize many industries across the world. However, artificial intelligence systems raise a series of ethical challenges that hamper their sustainable development. These challenges have often been discussed from a philosophical or theoretical perspective, but rarely from the more practical perspective of businesses. The lack of such a perspective for the ICT-sector in the Netherlands has formed a barrier to develop adequate ethical governance mechanisms. Specifically, the scarcity of practical data of ethical challenges and governance of business in the industry has made it difficult to establish the quality of the current code of ethics for the industry and the need for additional ethical governance measures. This research provides a descriptive study of ethical challenges and ethical governance of AI for ICT-businesses in the Netherlands. A form of mixed methodology (i.e., the exploratory sequential method) combines literature, interviews, and a questionnaire to acquire the relevant data. The results of these three methods are triangulated to identify room for improvement in the current code of ethics and identify the need for additional governance measures. Explainability, fairness, safety, and privacy were identified as the most pressing ethical challenges for businesses in the industry. This research also found that the first three of these challenges are currently insufficiently addressed in the code of ethics for the ICT-industry. Moreover, this study found a significantly low level of ethical governance of AI among small and medium-sized enterprises as compared to large companies. Future research should focus on further normative argumentation on principles for explainability, fairness, and safety to improve the code of ethics. Furthermore, this work lays the basis for researching and developing different concrete ethical governance measures of AI for the industry, especially for SMEs.

In a rapidly evolving digital landscape, where information is the currency of progress, universities play a vital role in fostering innovation, research, and knowledge dissemination. However, this invaluable role also exposes universities to significant cybersecurity challenges. Cybersecurity is an increasingly important topic for organisations in all sectors, including universities. As repositories of valuable research data and other sensitive information, universities are attractive targets for cyber attacks. Addressing these challenges is crucial not only to protect intellectual property and sensitive data but also to maintain the trust and integrity of academic institutions. Despite the importance of cybersecurity for universities, there is a lack of research on how to effectively implement cybersecurity policies and practices in this context. The lack of a standardised approach to cybersecurity can leave universities vulnerable to cyber threats and hinder the sharing of best practices. This study is expected to identify key challenges and measures for cybersecurity policy in Dutch universities. It will provide insights into the implementation of effective cybersecurity policies and contribute to the development of an approach to cybersecurity in the higher education sector. The research question is: "How should the cybersecurity policies of Dutch universities be designed to mitigate cyber threats to ensure knowledge security?". To accomplish this research, a multidimensional approach was adopted. Extensive literature review provided a foundation for understanding cybersecurity standards and cyber ethics, while interviews were conducted with various Dutch universities to gain insights into their experiences and perspectives. Additionally, a comprehensive survey was administered to students and staff members of Dutch universities, enriching the study with diverse viewpoints. This work aligns with the objective of examining the quality of decision-making concerning grand societal challenges within the context of their socio-economic and political environments. It aspires to inform decision-makers in the public (policy) domain or at the intersection of the public and private spheres. By shedding light on the barriers faced by universities in implementing cybersecurity norms, this research aims to contribute to the ongoing discourse on securing knowledge assets in the face of emerging cyber threats. The thesis is structured as follows: it begins with an introduction that provides a comprehensive overview of the topic, delineating the problem at hand and outlining the proposed approach. Subsequently, the literature review section presents the findings of the extensive research conducted, exploring various subjects such as cybersecurity standards and cyber ethics, among others. The expected outcomes of this study are an overview of current cybersecurity challenges for Dutch universities, an understanding of the opinions and experiences of university staff and students, and recommendations for developing and implementing cybersecurity policies for Dutch universities.

Water, an essential resource for diverse purposes like environmental protection, urban water supply, energy generation, and agriculture, faces intensifying demand amid depleting supplies. Multi-Objective Optimization (MOO)-models are vital for addressing complex water system challenges with limited resources. However, varying approaches to distributive justice in these models introduce normative bias, leading to uncertainty in the derived implications from the model. This thesis is the first approach to understanding how the operationalization of distributive justice shapes the implications drawn from the 'optimal' outcomes of MOO-models. This thesis studied: 'How do different operationalization formulations for inequality in existing multi-objective optimization models shift the Pareto front?'. A rival framings approach acknowledges diversity in perspectives, for which it is suitable to contrast the operationalization formulation. The rival framing focused on the inequality metric and the aggregation method over time for this metric, both used for the formulation of inequality in the objective formulation. The case study revolves around the Conowingo Reservoir in the Lower Susquehanna River Basin. Utilizing an Evolutionary Multi-Object Direct Policy Search (EMODPS)-model, the research optimizes water allocation by incorporating an equality objective alongside baseline efficiency goals. Results underscore that the trade-off between equality and efficiency is highly dependent on the chosen operationalization for equality. Moreover, for higher levels of equality, the Pareto front will shift drastically in terms of strength and direction of trade-offs. By unravelling the complexities of justice's integration into MOO-models, advances are made in the comprehension of how distributive justice can inform decision-making. Through the elaboration of justice formulations, a future is reachable where justice is not only considered but reached.

The introduction of artificial intelligence (AI) technologies in healthcare is expected to set a paradigm shift to medical practice because these systems will have a significant role in applications such as diagnosis-support and image analysis. However, this implementation does not come without risks. There are important ethical concerns that should be addressed beforehand to ensure public trust and acceptability. Privacy, safety, transparency, reliability and potential biases are some of the issues to consider. Responsible Research and Innovation (RRI) frameworks have been designed by academics to tackle this sort of problems but there is no application of these frameworks in the field of AI in healthcare. This problem is even more salient in the private sector, due to the unawareness of the RRI concept in industry. Consequently, the research objective of this project was to offer recommendations on how to implement RRI practices to avoid potential risks and improve the social acceptability of AI. For this, we studied the case of Philips and carried out interviews with the company’s experts in AI and corporate social responsibility (CSR). This information was complemented with a comprehensive study of the literature on topics related to AI in healthcare and RRI. The results from these activities were used to create a roadmap to introduce RRI practices in the AI innovation activities within Philips. The results showed that large companies should build upon their existing CSR practices to develop RRI. This will increase the acceptability of RRI within the research and development (R&D) teams. Based on that, we came up with 14 recommendations for the case of Philips. These actions range from current practices, such as continuing with the rigorous process of patient data selection and curation, to novel solutions such as including better interactive features in the design of telehealth platforms (i.e. virtual reality, video calls or social networks). Further research can be carried out in different companies to come up with common principles that contribute to the creation of a more comprehensive RRI framework for AI in healthcare.

Governments are increasingly dependent on GovTech, which is the technology that facilitates processes in the public sector. Benchmarking the state of GovTech is done by governments and yields indispensable insights, which are used for optimising resource utilisation, identifying areas for improvement, and facilitating evidence-based policy prioritisation. Current benchmarking efforts are resource-intensive, time-intensive, and have limited scope, resulting in an inefficient assessment of GovTech innovation. This thesis explores the potential of Large Language Models (LLMs) to overcome the practical limitations of existing GovTech benchmarking methods, analysing the GovTech Maturity Index of the World Bank as a case study. Using an LLM and leveraging state-of-the-art techniques, including fine-tuning, Retrieval Augmented Generation, and Prompt Engineering, the usability of these models as an artefact for GovTech benchmarking is assessed. The results show that the best-performing model outperforms the random chance accuracy, indicating that the LLM not only understands the question and data format, but also contains the information to correctly answer the benchmark questions. The research concludes that the created artefact has the potential to improve GovTech benchmarking, resulting in more informed policy decision making. The thesis contributes valuable insights to the field of the GovTech research field by making GovTech benchmarking more efficient, leading to a better analysis of the current GovTech market, and contributing to the academic debate on GovTech market analysis. Furthermore, by streamlining the Dutch benchmarking process, resulting in more accurate insights, the study contributes to the advancement of GovTech solutions within the Netherlands, ultimately benefiting society as a whole. The artefact created in this research has significant policy relevance, as more efficient benchmarking allows policymakers to better optimise resource allocation, identify key areas for investment, and improve evidence-based policy changes regarding GovTech. The limitations of this research include model inaccuracies, challenges in handling long contexts, and the potential for incorrect answers. An ethical analysis is performed, from which it can be concluded that the relevance of the information used by the models is the most apparent ethical concern. Future research may focus on improving the models to better handle long contexts, reducing inaccuracies, and improving the overall performance in populating GovTech benchmarks by incorporating additional data sources and improving the models. The research design is limited by the lack of environmental aspects in the process and the narrow scope of using a single benchmark for one country. As next steps, this research proposes a roadmap that includes the continuation of the development of the artefact, the extension of ethical analysis, the performance of trials, and the beginning of a wider application of the artefact.

Governments are increasingly dependent on GovTech, which is the technology that facilitates processes in the public sector. Benchmarking the state of GovTech is done by governments and yields indispensable insights, which are used for optimising resource utilisation, identifying areas for improvement, and facilitating evidence-based policy prioritisation. Current benchmarking efforts are resource-intensive, time-intensive, and have limited scope, resulting in an inefficient assessment of GovTech innovation. This thesis explores the potential of Large Language Models (LLMs) to overcome the practical limitations of existing GovTech benchmarking methods, analysing the GovTech Maturity Index of the World Bank as a case study. Using an LLM and leveraging state-of-the-art techniques, including fine-tuning, Retrieval Augmented Generation, and Prompt Engineering, the usability of these models as an artefact for GovTech benchmarking is assessed. The results show that the best-performing model outperforms the random chance accuracy, indicating that the LLM not only understands the question and data format, but also contains the information to correctly answer the benchmark questions. The research concludes that the created artefact has the potential to improve GovTech benchmarking, resulting in more informed policy decision making. The thesis contributes valuable insights to the field of the GovTech research field by making GovTech benchmarking more efficient, leading to a better analysis of the current GovTech market, and contributing to the academic debate on GovTech market analysis. Furthermore, by streamlining the Dutch benchmarking process, resulting in more accurate insights, the study contributes to the advancement of GovTech solutions within the Netherlands, ultimately benefiting society as a whole. The artefact created in this research has significant policy relevance, as more efficient benchmarking allows policymakers to better optimise resource allocation, identify key areas for investment, and improve evidence-based policy changes regarding GovTech. The limitations of this research include model inaccuracies, challenges in handling long contexts, and the potential for incorrect answers. An ethical analysis is performed, from which it can be concluded that the relevance of the information used by the models is the most apparent ethical concern. Future research may focus on improving the models to better handle long contexts, reducing inaccuracies, and improving the overall performance in populating GovTech benchmarks by incorporating additional data sources and improving the models. The research design is limited by the lack of environmental aspects in the process and the narrow scope of using a single benchmark for one country. As next steps, this research proposes a roadmap that includes the continuation of the development of the artefact, the extension of ethical analysis, the performance of trials, and the beginning of a wider application of the artefact.

This research explores the use of Artificial Intelligence (AI), specifically Large Language Models (LLMs), into the operationalization of Government Technology (GovTech) benchmarks to increase their utility for policymakers. Research and practice consistently highlight persistent challenges in GovTech benchmarking, such as resource-intensive methodologies that provide retrospective rather than real-time analysis, a lack of complexity that overlooks digital infrastructures and emerging technologies in favor of simpler metrics, and improper levels of aggregation that can render results less useful. Considering that benchmarks can significantly influence political outcomes and shape the development of GovTech services, refining benchmarking methodologies using LLMs can mitigate these issues and potentially improve the responsiveness and relevance of government actions that better serve societal needs. Using Design Science Research Methodology and Activity Theory, an artefact is developed that combines an LLM with Retrieval-Augmented Generation (RAG), fine-tuning and prompt-engineering. The artefact is used to operationalize the GTMI-benchmark by the World Bank. The development of a benchmark specifically tailored for operationalization by LLMs is proposed, with a preliminary design for an AI-Supported GovTech Index (AGTI) outlined.

The consumer lending domain has increasingly leveraged Artificial Intelligence (AI) to make loan approval processes more efficient and to make use of larger amount of information to predict their applicants’ repayment ability. Over time, however, valid concerns have been raised about whether decisions made about individuals using these data-driven technologies can lead to bias against women. In an attempt to assess the fairness of an algorithm, 21 prominent definitions of fairness have been proposed by the computer science community over the years. However, what remains absent is consensus on which definitions are suitable for assessing gender equality in consumer lending. There is also a lack of knowledge on how to appropriately implement these metrics in practice. To tackle the problems mentioned above, this research has investigated how automated loan approval processes can be assessed for gender equality. Two essential elements for assessing predictive tools were identified and investigated through a separate research question: What fairness metrics are suitable for assessing gender equality in consumer lending? How can the metrics be applied to observe gender bias in lending history data? Based on the questions above, the research was conducted in two stages: Stage 1 focused on analyzing the prominent definitions of group fairness, but before doing so, it conceptualizes gender equality in consumer lending by conducting an extensive literature review encompassing domains of philosophy, economics, gender studies, and history. In investigating the first research question, it is found that group fairness metrics are a measure of distributive justice. These metrics are based on three different statistical criteria commonly known as independence, sufficiency, and separation and each underlie different moral assumptions which should be verified based on the application scenario at hand. In Stage 2 of this work, the second research question was investigated by conducting an exploratory case study in which a logistic regression model is built to classify a sample of loan applicants in an open source dataset. Both the dataset and the model are then tested for bias using IBM’s open source Python AIF36 toolkit. After applying the group fairness metrics, it was found that the choice of separation and sufficiency can have different repercussions for each demographic group in the dataset. When false distribution of utility is under inspection, sufficiency advantaged male applicants more than the female applicants while separation advantages males more than females. Such inconsistency highlights the importance of realizing how relevant distribution of harm/benefit depends on the choice of fairness criteria made by decision makers. Lastly, the research provides an extensive discussion on possible root causes of bias and some recommendations to managers and data stewards on how to tackle bias issues that stakeholders may face in the context of consumer lending.

The advancement of AI-based technologies, such as machine learning (ML) systems, for implementation in healthcare is progressing rapidly. Since these systems are used to support healthcare professionals in crucial medical practices, their role in medical decision-making needs to be epistemologically and ethically assessed. However, a central issue at the intersection of the ethics and epistemology of ML has been largely neglected. This pertains to the careful scrutiny of how ML systems can degrade individuals’ epistemic standing as receivers and conveyors of knowledge and, thereby, perpetrate epistemic injustice. Since ML systems are powerful epistemic entities that are not easily contestable, and their decision-making rationale is often inaccessible, it is crucial to consider their role in creating imbalances in patients’ disfavor and the ways to mitigate such imbalances. This is especially important when it comes to interactions between patients and physicians, in which questions of credibility, trust, and understanding are central. Against this background, the overarching purpose of this dissertation is to fill this research gap by providing a framework to identify and, on occasion, mitigate epistemic injustices that are ML-induced, i.e., that emerge specifically due to the role that ML systems play in patient-physician interactions.@en