Self-tracking-technologies can serve as a prominent example of how digital technologies put to test established practices, institutions, and structures of medicine and healthcare. While proponents emphasize the potentials, e.g., for individualized healthcare and new research data, opponents stress the risk that these technologies will reinforce gender-related inequalities. -/- While this has been made clear from—often intersectional—feminist perspectives since the introduction of such technologies, we aim to provide a queer-feminist perspective on self-tracking applications in healthcare by analyzing three concrete cases. In (...) this way, we want to show why such a perspective is helpful in assessing self-tracking technologies and how such a perspective might contribute to a queering of healthcare. -/- After a short introduction to self-tracking in healthcare and the corresponding risks of reproducing gender-related inequalities, we introduce queer bioethics as a methodology to examine structural discrimination and marginalization in bioethical research. We apply this framework in the analysis of three exemplary self-tracking applications from the areas of nutrition/fitness, reproductive health, and mental health. In the subsequent discussion of these results against the backdrop of already existing queer-sensitive technologies, we identify an equal access, a subversive design, an inclusive database, and a diversity-sensitive and discriminatory-critical definition of purposes as well as individualized usage possibilities as first possible starting points for a queering of self-tracking applications. Finally, we draw conclusions, how such a critical perspective on self-tracking applications might also contribute to a queering of the healthcare system at a structural level. (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)

Intersectionality has become a buzzword in the humanities, yet it often remains ill-defined or is used inconsistently depending on the subject area. On the one hand, intersectionality still needs refinement and lacks scientific acknowledgement. Particularly in German-speaking theological ethics, intersectionality has been little discussed. On the other hand, intersectionality seems to be so established, especially in the Anglo-American context, that the intersection of gender and race is far from questioned anymore. However, both stances overlook the inherent dimensions of intersectionality, failing (...) to effectively tackle the challenges posed by algorithmic classification. These challenges stem from the ongoing creation of new intersections and the superimposition or obscuration of existing ones. However, intersectionality understood as a knowledge project promises to address the challenges effectively. Therefore, the paper identifies key dimensions of what constitutes intersectionality. First, intersectional theories aim to de-marginalize the intersection where discrimination and injustice occur. Second, intersectional theories identify the intersections as multiple, non-hierarchical, and dynamic entanglements. Third, intersectional theories are often misunderstood as identity politics. However, they function as a critique of power and the pursuit of justice. From these three dimensions of intersectional theories, the paper draws socio-ethical implications for algorithmic classification. The in-depth analysis shows that intersectional theory is able to expose the difficulties of using categories and classifiers as well as aggregating and disaggregating data in the context of Artificial Intelligence. The paper points out that the disaggregation of data and fine-grained analysis can only take place where political protection mechanisms ensure that individuals and groups are not harmed. (shrink)