New data-driven technologies yield benefits and potentials, but also confront different agents and stakeholders with challenges in retaining control over their data. Our goal in this study is to arrive at a clear picture of what is meant by data sovereignty in such problem settings. To this end, we review 341 publications and analyze the frequency of different notions such as data sovereignty, digital sovereignty, and cyber sovereignty. We go on to map agents they concern, in which context they appear, (...) and which values they allude to. While our sample reveals a considerable degree of divergence and an occasional lack of clarity about intended meanings of data sovereignty, we propose a conceptual grid to systematize different dimensions and connotations. Each of them relates in some way to meaningful control, ownership, and other claims to data articulated by a variety of agents ranging from individuals to countries. Data sovereignty alludes to a nuanced mixture of normative concepts such as inclusive deliberation and recognition of the fundamental rights of data subjects. (shrink)

In their contribution ‘Ethics of the algorithmic prediction of goal of care preferences’1 Ferrario et al elaborate a from theory to practice contribution concerning the realisation of artificial intelligence (AI)-based patient preference prediction (PPP) systems. Such systems are intended to help find the treatment that the patient would have chosen in clinical situations—especially in the intensive care or emergency units—where the patient is no longer capable of making that decision herself. The authors identify several challenges that complicate their effective development, (...) application and evaluation—and offer solutions to them. One of these issues is the question of who should ultimately use said systems. While it is undisputed that clinicians should use these AI systems for their decision-making process, there is an ongoing debate about whether next of kin should use them as well. The authors advocate that ‘’access should be provided to both clinicians and loved ones with due explanations and as desired’. We will disagree with this assessment and explain in our commentary why it is important that surrogates provide their own assessments with as little external (AI) influence as possible. Why do next of kin actually participate in the process of preference finding and treatment decision-making? A key reason is that clinicians usually …. (shrink)

In his contribution, ‘Represent me: please! Towards an ethics of digital twins in medicine’1, Braun shows that there is a fundamental ambivalence inherent in digital twins: they can either open up new freedoms for the simulated persons, or, conversely, endanger and restrict their freedom. To prevent digital twins from restricting people’s freedom, Braun suggests a strong focus on control. Braun’s focus on control is insufficient, I will argue, because his idea of control works only retroactively: it can only react to (...) restrictions of freedom that have already occurred, but it cannot prevent them, nor can it restore any lost freedom. Control is therefore insufficient to defend people’s freedom. To prove my claim, I will first use an everyday example to illustrate the fundamental ambivalence inherent in digital twins. Second, I will show that digital twins endanger people’s freedom in a hermeneutic way. Finally, I will demonstrate why controllability cannot prevent this hermeneutic endangerment. I understand digital twins, like Braun, as simulation of a person, as their dynamic and precise effigy. Simulated twins use machine-learning and artificial intelligence capabilities to make predictive analyses about the simulated person and the future development of her health. Imagine: You are eating dinner. Meanwhile, your partner lectures you about the calorie density and nutrient composition of your food, warns you that your portion size exceeds your daily calorie requirement by x calories. It calculates how your dinner will negatively impact your body mass index and physical fitness, and predicts what the consequences will be for your overall health. Based on these data and predictions, it creates an individualised diet plan for you—and at the same time shows you by how much you could improve your vital values by sticking to that plan. This example helps to illustrate the fundamental …. (shrink)

Consent-GPT: is it ethical to delegate procedural consent to conversational AI? In their article ‘Consent-GPT: is it ethical to delegate procedural consent to conversational AI?’, Allen _et al_ 1 explore the ethical complexities involved in handing over parts of the process of obtaining medical consent to conversational Artificial Intelligence (AI) systems, that is, AI-driven large language models (LLMs) trained to interact with patients to inform them about upcoming medical procedures and assist in the process of obtaining informed consent. 1 They (...) focus specifically on challenges related to accuracy (4–5), trust (5), privacy (5), click-through consent (5) and responsibility (5–6), alongside some pragmatic considerations (6). While the authors competently navigate these critical issues and present several key perspectives, we posit that their discussion on trust in what they refer to as ‘Consent-GPT’ significantly underestimates one vital factor: the interpersonal aspect of trust. Admittedly, this interpersonal aspect is... (shrink)

Background The use of Artificial Intelligence (AI) has the potential to provide relief in the challenging and often stressful clinical setting for physicians. So far, however, the actual changes in work for physicians remain a prediction for the future, including new demands on the social level of medical practice. Thus, the question of how the requirements for physicians will change due to the implementation of AI is addressed. Methods The question is approached through conceptual considerations based on the potentials that (...) AI already offer and the focus on central normative concepts of trust, explainability, and responsibility which play an important role when implementing AI in everyday clinical practice. Conclusion Interpersonal communication will not disappear upon implementation of AI. Instead, it is much more likely that the exchange between various actors in medical practice will become increasingly important. This adds another level of complexity to practical concepts such as Shared-Decision-Making, which must be addressed in empirical research, including the involvement of AI systems as actors in communication. (shrink)

Since uncertainty is a major challenge in medicine and bears the risk of causing incorrect diagnoses and harmful treatment, there are many efforts to tackle it. For some time, AI technologies have been increasingly implemented in medicine and used to reduce medical uncertainties. What initially seems desirable, however, poses challenges. We use a multimethod approach that combines philosophical inquiry, conceptual analysis, and ethical considerations to identify key challenges that arise when AI is used for medical certainty purposes. We identify several (...) challenges. Where AI is used to reduce medical uncertainties, it is likely to result in (a) patients being stripped down to their measurable data points, and being made disambiguous. Additionally, the widespread use of AI technologies in health care bears the risk of (b) human physicians being pushed out of the medical decision-making process, and patient participation being more and more limited. Further, the successful use of AI requires extensive and invasive monitoring of patients, which raises (c) questions about surveillance as well as privacy and security issues. We outline these several challenges and show that they are immediate consequences of AI-driven security efforts. If not addressed, they could entail unfavorable consequences. We contend that diminishing medical uncertainties through AI involves a tradeoff. The advantages, including enhanced precision, personalization, and overall improvement in medicine, are accompanied by several novel challenges. This paper addresses them and gives suggestions about how to use AI for certainty purposes without causing harm to patients. (shrink)