More and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this (...) the problem of radical ignorance. Interestingly, radical ignorance exists in consent contexts other than AI, where it seems that individuals can provide informed consent. The article argues that radical ignorance can undermine informed consent in some contexts but not others because, under certain institutional, autonomy-protecting conditions, consent can be valid without being (perfectly) informed. By understanding these institutional conditions, we can formulate practical solutions to foster valid, albeit imperfectly informed consent across various decision contexts and within different institutions. (shrink)

This paper analyzes the ethics of social science research employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: the interpretative character of both SSR and big data, complexities of anticipating and managing risks in publication and reuse of big data SSR, and the paucity of regulatory oversight and (...) ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR. (shrink)

Although big-data research has met with multiple controversies in diverse fields, political and security implications of big data in life sciences have received less attention. This paper explores how threats and risks are anticipated and acted on in biobanking, which builds research repositories for biomedical samples and data. Focusing on the biggest harmonisation cluster of biomedical research in Europe, BBMRI-ERIC, the paper analyses different logics of risk in the anticipatory discourse on biobanking. Based on document analysis, interviews with ELSI experts, (...) and field research, three types of framing of risk are reconstructed: data security, privacy, and data misuse. The paper finds that these logics downplay the broader social and political context and reflects on the limits of the practices of anticipatory governance in biobanking. It argues that this regime of governance can make it difficult for biobanks to address possible future challenges, such as access to biomedical data by authorities, pressures for integrating biobank data with other type of personal data, or their use for profiling beyond medical purposes. To address potential controversies and societal implications related to the use of big data in health research and medicine, the paper suggests to expand the vocabulary and practices of anticipatory governance, in the biobanking community and beyond. (shrink)

Mobile devices with health apps, direct-to-consumer genetic testing, crowd-sourced information, and other data sources have enabled research by new classes of researchers. Independent researchers, citizen scientists, patient-directed researchers, self-experimenters, and others are not covered by federal research regulations because they are not recipients of federal financial assistance or conducting research in anticipation of a submission to the FDA for approval of a new drug or medical device. This article addresses the difficult policy challenge of promoting the welfare and interests of (...) research participants, as well as the public, in the absence of regulatory requirements and without discouraging independent, innovative scientific inquiry. The article recommends a series of measures, including education, consultation, transparency, self-governance, and regulation to strike the appropriate balance. (shrink)

This article examines the privacy and security issues associated with mobile application-mediated health research, concentrating in particular on research conducted or participated in by independent scientists, citizen scientists, and patient researchers. Building on other articles in this issue that examine state research laws and state data protection laws as possible sources of privacy and security protections for mobile research participants, this article focuses on the lack of application of federal standards to mobile application-mediated health research. As discussed in more detail (...) below, the voluminous and diverse data collected by some independent scientists who use mobile applications to conduct health research may be at risk for unregulated privacy and security breaches, leading to dignitary, psychological, and economic harms for which participants have few legally enforceable rights or remedies under current federal law. Federal lawmakers may wish to consider enacting new legislation that would require otherwise unregulated health data holders to implement reasonable data privacy, security, and breach notification measures. (shrink)

Researchers now commonly collect biospecimens for genomic analysis together with information from mobile devices and electronic health records. This rich combination of data creates new opportunities for understanding and addressing important health issues, but also intensifies challenges to privacy and confidentiality. Here, we elucidate the “web” of legal protections for precision medicine research by integrating findings from qualitative interviews with structured legal research and applying them to realistic research scenarios involving various privacy threats.

May 21, 2018, marks the tenth anniversary of the signing into law of the Genetic Information Nondiscrimination Act. The Congressional deliberations for GINA were long and difficult. The original bill was introduced in 1995, and for many years, it did not look as if the bill would ever emerge from committee. Several of its provisions raised concerns for insurers, employers, and other stakeholders. After thirteen years, the controversial provisions were either deleted, revised, or clarified. At this ten‐year mark, it is (...) appropriate to take stock of GINA. In light of GINA's glacial legislative history, it is reasonable to start thinking about the necessity, wisdom, and feasibility of amending GINA or enacting new legislation to address unresolved or emerging issues of genetic discrimination and trends in genetics, genomics, precision medicine, and related technologies. (shrink)

Biomedical innovation and translation are increasingly emphasizing research using “big data.” The hope is that big data methods will both speed up research and make its results more applicable to “real-world” patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, (...) and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals. (shrink)

Purpose The purpose of this study is to examine how the adoption of blockchain technology can improve organizational sustainability and what are the contributions of morality, ethics and governance in this scenario. Design/methodology/approach This study has used different literature and theories to build a successful theoretical model and then validated it using the partial least squares structural equation modeling approach. Various statistical modeling analyses have been performed to test the robustness of the proposed model, which is found to be effective (...) and unique as it has a high explanatory power. Findings This study has found that adoption of blockchain technology can improve the sustainability performance of organizations. It has also highlighted that organizational sustainability will be further enhanced if the underlying algorithms of blockchain technology are transparent to make the applications moral and ethical. This study has further demonstrated that appropriate governance of blockchain technology can support the organizations to enhance their sustainability performance. Research limitations/implications This study demonstrates the importance of an organization’s governance over blockchain technology and its impact on sustainability. The study provides valuable input to practitioners and policy makers regarding blockchain technology and its governance models. Also, the study provides valuable recommendations to leaders and managers of organizations on how to successfully adopt blockchain technology. Originality/value This study presents a unique theoretical model to explain the influence of the adoption of blockchain technology and an organization’s sustainability. The study also adds value to the overall body of literature in the areas of ethics, governance and blockchain technology along with sustainability. (shrink)

Background Bioethical debates about privacy, big data, and public health surveillance have not sufficiently engaged the perspectives of those being surveilled. The data justice framework suggests that big data applications have the potential to create disproportionate harm for socially marginalized groups. Using examples from our research on HIV surveillance for individuals incarcerated in jails, we analyze ethical issues in deploying big data in public health surveillance. -/- Methods We conducted qualitative, semi-structured interviews with 24 people living with HIV who had (...) been previously incarcerated in county jails about their perspectives on and experiences with HIV surveillance, as part of a larger study to characterize ethical considerations in leveraging big data techniques to enhance continuity of care for incarcerated people living with HIV. -/- Results Most participants expressed support for the state health department tracking HIV testing results and viral load data. Several viewed HIV surveillance as a violation of privacy, and several had actively avoided contact from state public health outreach workers. Participants were most likely to express reservations about surveillance when they viewed the state’s motives as self-interested. Perspectives highlight the mistrust that structurally vulnerable people may have in the state’s capacity to act as an agent of welfare. Findings suggest that adopting a nuanced, context-sensitive view on surveillance is essential. -/- Conclusions Establishing trustworthiness through interpersonal interactions with public health personnel is important to reversing historical legacies of harm to racial minorities and structurally vulnerable groups. Empowering stakeholders to participate in the design and implementation of data infrastructure and governance is critical for advancing a data justice agenda, and can offset privacy concerns. The next steps in advancing the data justice framework in public health surveillance will be to innovate ways to represent the voices of structurally vulnerable groups in the design and governance of big data initiatives. (shrink)