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.

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daher die Pflicht zur Vorlage der eGK -mit Lichtbild -zu Identifizierungszwecken unberührt lassen wird (vgl. § 291 Abs. 1 SGB V).Im Ergebnis ist die sukzessive Digitalisierung des Gesundheitssektors zweifellos als Mehrwert, allem voran aber als wichtiger Schritt zu begreifen, bei dem datenschutzund verfassungsrechtliche Zweifel oftmals unvermeidbar zugunsten einer flächendeckenden, effizienten sowie höchste technische wie medizinische Standards gewährende Gesundheitsversorgung zurücktreten müssen.Open Access. Dieser Artikel wird unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden. Die in diesem Artikel enthaltenen Bilder und sonstiges Drittmaterial unterliegen ebenfalls der genannten Creative Commons Lizenz, sofern sich aus der Abbildungslegende nichts anderes ergibt. Sofern das betreffende Material nicht unter der genannten Creative Commons Lizenz steht und die betreffende Handlung nicht nach gesetzlichen Vorschriften erlaubt ist, ist für die oben aufgeführten Weiterverwendungen des Materials die Einwilligung des jeweiligen Rechte inhabers einzuholen. Weitere Details zur Lizenz entnehmen Sie bitte der Lizenzinformation auf http://creativecommons.org/licenses/by/4.0/deed.de.Open Access funding enabled and organized by Projekt DEAL.

Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.

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BackgroundIn the context of the COVID-19 pandemic, there is much discussion about contact tracing apps, their use to contain the spread of the virus as well as the ethical, legal, and social aspects of their development, implementation, acceptance, and use. In these discussions, authors frequently mention “solidarity” when making key points in arguments. At the same time, authors rarely specify how they understand “solidarity”. This lack of specification about how they understand “solidarity” can lead to misunderstandings in discussions.ObjectiveTo prevent such misunderstandings, it is important to specify how one understands “solidarity” when mentioning it in the discussions on contact tracing apps. Therefore, the aim of this paper is to elaborate how “solidarity” is understood in the context of contact tracing apps, i.e., how different authors understand “solidarity” when using it in discussions about these apps.MethodsIn order to find out how different authors understand “solidarity” when discussing contact tracing apps, I conduct a literature review. I collect papers from several databases, inductively work out central differences and similarities between the different uses of “solidarity”, and use them to code and analyze relevant passages.ResultsIn the final sample, five different understandings of “solidarity” in the context of contact tracing apps can be identified. These understandings differ in how different authors (1) imagine the basic concept of solidarity, i.e., what “solidarity” refers to, (2) how they temporally relate solidarity to contact tracing apps, and (3) how they perceive the causal interactions between solidarity and contact tracing apps, i.e., the different ways in which solidarity and contact tracing apps influence each other.ConclusionsThe five understandings of “solidarity” in the context of contact tracing apps presented here can serve as guidance for how “solidarity” can be understood in discussions—thus contributing to a better mutual understanding and preventing communicative misunderstandings.