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In recent years, critical scholarship has drawn attention to increasing power differentials between corporations that use data and people whose data is used. A growing number of scholars see digital data and information commons as a way to counteract this asymmetry. In this paper I raise two concerns with this argument: First, because digital data and information can be in more than one place at once, governance models for physical common-pool resources cannot be easily transposed to digital commons. Second, not all data and information commons are suitable to address power differentials. In order to create digital commons that effectively address power asymmetries we must pay more systematic attention to the issue of exclusion from digital data and information commons. Why and how digital data and information commons exclude, and what the consequences of such exclusion are, decide whether commons can change power asymmetries or whether they are more likely to perpetuate them.

  The \"big society\" vision of some political parties is an example: citizens who help themselves and others because national and local governments have stopped providing infrastructures are applauded for being active participants in society, while those who are not strong enough to help themselves can be dismissed as unwilling. [...]it is possible that patients make contributions--in terms of time, data, or information--that other entities will profit from, without receiving any share in these profits, also under the label of \"participatory medicine\" or patient \"empowerment\"; this is what led to the \"patent crisis\" around 23andMe [3],[4]. [...]we need to take a close look at how power and agency are distributed in a specific context or account of participatory medicine:

The main German-speaking countries (Germany, Austria, and Switzerland) have implemented digital contact tracing apps to assist the authorities with COVID-19 containment strategies. Low user rates for these apps can affect contact tracing and, thus, its usefulness in controlling the spread of the novel coronavirus. This study aimed to assess the early perceptions of people living in the German-speaking countries and compare them with the frames portrayed in the newspapers during the first wave of the COVID-19 pandemic. We conducted qualitative interviews with 159 participants of the SolPan project. Of those, 110 participants discussed contact tracing apps and were included in this study. We analyzed articles regarding contact tracing apps from 12 newspapers in the German-speaking countries. Study participants perceived and newspaper coverage in all German-speaking countries framed contact tracing apps as governmental surveillance tools and embedded them in a broader context of technological surveillance. Participants identified trust in authorities, respect of individual privacy, voluntariness, and temporary use of contact tracing apps as prerequisites for democratic compatibility. Newspapers commonly referenced the use of such apps in Asian countries, emphasizing the differences in privacy regulation among these countries. The uptake of digital contact tracing apps in German-speaking countries may be undermined due to privacy risks that are not compensated by potential benefits and are rooted in a deeper skepticism towards digital tools. When authorities plan to implement new digital tools and practices in the future, they should be very transparent and proactive in communicating their objectives and the role of the technology-and how it differs from other, possibly similar, tools. It is also important to publicly address ethical, legal, and social issues related to such technologies prior to their launch.

The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the ‘distorted’ outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable—exactly because they can contribute to overcome inequities.

Background Genetic diagnosis is often understood as a single event within the care pathway of rare disease patients. Legal, policy and ethical scholarship focusing on rare diseases and genetic information discusses questions of how to best deal with the process of genetic diagnosis and the communication of genetic information within a given health system. We co-created a research design with rare disease patients and their families in Austria to explore in-depth the experiences of genetic diagnosis for people affected by rare diseases. Our objective was to trace the whole pathway of genetic testing and understand how rare disease patients experience genetic diagnosis as part of their care pathway in the healthcare system. Results Data was collected through in-depth semi-structured qualitative interviews with 14 patients with a suspected or diagnosed rare disease or their parents, focusing on their perception of the pathway of genetic diagnosis in Austria. This pathway included the initial triggering of genetic diagnosis, the process of testing and its immediate (communication of results, counselling) and long-term, wider aftermath. Patients missed a clear link to already established forms of care such as their primary care/treating physicians. They also advocate for an integrated and interdisciplinary care pathway. Conclusions Our study underscores the importance of a continuous care and communication pathway spanning from the initial genetic diagnosis process to post-test phases. It further shows the importance of exploring patients’ perspectives through qualitative research methods to understand the intricate workings of public health policies and tools. Integrating genetic diagnosis into a broader care trajectory is crucial for a holistic approach to care for rare disease patients who often rely on regular interactions with the healthcare system. Achieving this holistic approach requires collaboration between experts in specific rare disease areas, primary care physicians, and support networks.

Trust may be important in shaping public attitudes to genetics and intentions to participate in genomics research and big data initiatives. As such, we examined trust in data sharing among the general public. A cross-sectional online survey collected responses from representative publics in the USA, Canada, UK and Australia (n = 8967). Participants were most likely to trust their medical doctor and less likely to trust other entities named. Company researchers were least likely to be trusted. Low, Variable and High Trust classes were defined using latent class analysis. Members of the High Trust class were more likely to be under 50 years, male, with children, hold religious beliefs, have personal experience of genetics and be from the USA. They were most likely to be willing to donate their genomic and health data for clinical and research uses. The Low Trust class were less reassured than other respondents by laws preventing exploitation of donated information. Variation in trust, its relation to areas of concern about the use of genomic data and potential of legislation are considered. These findings have relevance for efforts to expand genomic medicine and data sharing beyond those with personal experience of genetics or research participants.

This article challenges a key tacit assumption underpinning legal and ethical instruments in health care, namely, that people are ideally bounded, independent, and often also strategically rational individuals. Such an understanding of personhood has been criticized within feminist and other critical scholarship as being unfit to capture the deeply relational nature of human beings. In the field of medicine, however, it also causes tangible problems. I propose that a solidarity-based perspective entails a relational approach and as such helps to formulate new solutions to complex ethical and regulatory questions, ranging from caring for people at the end of their lives to improving policies for organ donation and better governance of health data. It also underscores the importance of universal health care. Although a solidarity-based perspective does not require health to be seen as an individually enforceable right, it does influence our understanding of individual rights: it draws attention to how their meaning is shaped by shared social practices. I conclude by arguing that, in light of current pressures for medicine to become more personalized, using a relational understanding of personhood to shape policies and practices is a much needed endeavor.

Abstract This short article discusses how the COVID-19 crisis has affected solidarity. It starts by defining solidarity in such a way that it can be distinguished from other types of support and pro-social practice, and by arguing that solidarity can manifest itself at three different levels: at the inter-personal level, the group level, or at the level of legal and contractual norms. Drawing upon findings from two ongoing studies on personal and societal effects of the COVID-19 crisis, I then go on to argue that, while forms of inter-personal solidarity have been shifting even during the first weeks and months of the crisis, the importance of institutionalized solidarity is becoming increasingly apparent. The most resilient societies in times of COVID-19 have not been those with the best medical technology or the strictest pandemic containment measures, but those with good public infrastructures and other solidaristic institutions.

This paper reports findings from Germany-based participants in the “Your DNA, Your Say” study, a collaborative effort among researchers in more than 20 countries across the world to explore public attitudes, values and opinions towards willingness to donate genomic and other personal data for use by others. Based on a representative sample of German residents (n = 1506) who completed the German-language version of the survey, we found that views of genetic exceptionalism were less prevalent in the German-language arm of the study than in the English-language arm (43% versus 52%). Also, people’s willingness to make their data available for research was lower in the German than in the English-language samples of the study (56% versus 67%). In the German sample, those who were more familiar with genetics, and those holding views of genetic exceptionalism were more likely to be willing to donate data than others. We explain these findings with reference to the important role that the “right of informational self-determination” plays in German public discourse. Rather than being a particularly strict interpretation of privacy in the sense of a right to be left alone, the German understanding of informational self-determination bestows on each citizen the responsibility to carefully consider how their personal data should be used to protect important rights and to serve the public good.

Here, we argue that, in line with the dramatic increase in the collection, storage and curation of human genomic data for biomedical research, genomic data repositories and consortia have adopted governance frameworks to both enable wide access and protect against possible harms. However, the merits and limitations of different governance frameworks in achieving these twin aims are a matter of ongoing debate in the scientific community; indeed, best practices and points for consideration are notably absent in devising governance frameworks for genomic databases. According to our collective experience in devising and assessing governance frameworks, we identify five key functions of ‘good governance’ (or ‘better governance’) and three areas in which trade-offs should be considered when specifying policies within those functions. We apply these functions as a benchmark to describe, as an example, the governance frameworks of six large-scale international genomic projects.