The effects of digital technologies on freedom and democracy have garnered increasing attention in recent years. Many have raised concerns about surveillance capitalism, technofeudalism, and general threats to constitutional democracies—with a special convergence on the worry that uncontrolled power of online platforms undermines people’s freedom. However, it remains unclear how ‘freedom’ should be understood, what the relation is between freedom and uncontrolled power, and to what extent these worries extend beyond online platforms. In this dissertation, I argue that these problems are best answered by appealing to a neo-republican account of freedom as non-domination, where ‘domination’ is understood as a condition of living under an agent’s uncontrolled power. In the context of AI systems used in core societal sectors such as healthcare, I show that domination of a system’s (in)direct end-users by the system’s developers occurs in at least three ways: (1) the distribution of decision-making power, (2) technical limitations of AI systems, and (3) underlying societal structures that empower developers and disempower end-users. To safeguard freedom in the digital age, I propose that AI development requires the explicit intention to 'design for non-domination'. This requires us to consider the broader societal contexts within which these systems operate, such as current regulatory initiatives and the political economy.@en

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.

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 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.