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Background: Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts., Main Text: In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC's scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC's way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science., Conclusions: We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

The increased use of big data in the medical field has shifted the way in which biomedical research is designed and carried out. The novelty of techniques and methods brought by big data research brings new challenges to institutional review boards (IRBs). Yet it is unclear if IRBs should be the responsible oversight bodies for big data research and, if so, which criteria they should use. A large but heterogenous set of ethics guidelines and normative responses have emerged to address these issues. In this study, we conducted a scoping review of soft-law documents and guidelines with the aim of assessing ongoing normative efforts that are proliferating in this domain. We also synthesize a set of recurrent guidelines that could work as a baseline to create a harmonized process for big data research ethics., (© 2020 by The Hastings Center. All rights reserved.)

The past few years have witnessed several media-covered cases involving citizens actively engaging in the pursuit of experimental treatments for their medical conditions-or those of their loved ones-in the absence of established standards of therapy. This phenomenon is particularly observable in patients with rare genetic diseases, as the development of effective therapies for these disorders is hindered by the limited profitability and market value of pharmaceutical research. Sociotechnical trends at the cross-section of medicine and society are facilitating the involvement of patients and creating the digital infrastructure necessary to its sustainment. Such participant-led research (PLR) has the potential to promote the autonomy of research participants as drivers of discovery and to open novel non-canonical avenues of scientific research. At the same time, however, the extra-institutional, self-appointed, and, often, oversight-free nature of PLR raises ethical concern. This paper explores the complex ethical entanglement of PLR by critically appraising case studies and discussing the conditions for its moral justification. Furthermore, we propose a path forward to ensure the safe and effective implementation of PLR within the current research ecosystem in a manner that maximizes the benefits for both individual participants and society at large, while minimizing the risks.

While genetic testing may be the gateway to the future of medicine, it also poses challenges for individuals, especially in terms of differentiated treatments on the basis of their genetic characteristics. The fear of unwanted disclosure to insurers and the possibility of genetic discrimination can hamper the recruitment of individuals for clinical research that involves genetic testing. Precision medicine initiatives, such as All of Us, are proliferating in the United States. In order to succeed, however, they must ensure that the millions of Americans recruited to share their genetic data are not penalized with regard to life, disability and long-term insurance coverage. In this Perspective, we discuss several initiatives adopted by countries around the world, such as the United Kingdom and France, that better balance the interests of insurers and research subjects, and explain how the United States might learn from them. We call for regulatory and industry leadership to come together to establish a voluntary moratorium on insurance pricing with the aim of protecting research participants.

Dual use research of concern (DURC) is defined by the World Health Organization as "life sciences research that is intended for benefit, but which might easily be misapplied to do harm." Ethical and policy discussions on DURC span the past three decades. Today, however, new and emerging technologies and associated sociocultural transformations within the scientific community are reshaping the current risk scenario. This paper identifies three major trends that are likely to characterise dual use dilemmas in the near future: the diversification of dual use domains, the digitalisation of potential threats and the proliferation of actors. This analysis illustrates an increasingly heterogeneous and fragmented risk scenario, which can hardly be effectively governed top-down from a centralised authority. We propose that in order to meet the critical challenges of dual use in the 21st century, a global and distributed governance is needed. In contrast to globally binding sets of legal mechanisms administered by a central and hierarchical authority supported by leading powers, we suggest a global and decentralised governance architecture encompassing multilevel, multipolar and bottom-up strategies that can stretch across a spectrum of stakeholders and scientific domains in an agile, proactive and adaptive manner. Finally, we discuss how Switzerland can take a leading role in the promotion and development of this global governance architecture.

This paper poses the question of whether people have a duty to participate in digital epidemiology. While an implied duty to participate has been argued for in relation to biomedical research in general, digital epidemiology involves processing of non-medical, granular and proprietary data types that pose different risks to participants. We first describe traditional justifications for epidemiology that imply a duty to participate for the general public, which take account of the immediacy and plausibility of threats, and the identifiability of data. We then consider how these justifications translate to digital epidemiology, understood as an evolution of traditional epidemiology that includes personal and proprietary digital data alongside formal medical datasets. We consider the risks imposed by re-purposing such data for digital epidemiology and propose eight justificatory conditions that should be met in justifying a duty to participate for specific digital epidemiological studies. The conditions are then applied to three hypothetical cases involving usage of social media data for epidemiological purposes. We conclude with a list of questions to be considered in public negotiations of digital epidemiology, including the application of a duty to participate to third-party data controllers, and the important distinction between moral and legal obligations to participate in research.

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.

Precision medicine promises to develop diagnoses and treatments that take individual variability into account. According to most specialists, turning this promise into reality will require adapting the established framework of clinical research ethics, and paying more attention to participants' attitudes towards sharing genotypic, phenotypic, lifestyle data and health records, and ultimately to their desire to be engaged as active partners in medical research.Notions such as participation, engagement and partnership have been introduced in bioethics debates concerning genetics and large-scale biobanking to broaden the focus of discussion beyond individual choice and individuals' moral interests. The uptake of those concepts in precision medicine is to be welcomed. However, as data and medical information from research participants in precision medicine cohorts will be collected on an individual basis, translating a participatory approach in this emerging area may prove cumbersome. Therefore, drawing on Joseph Raz's perfectionism, we propose a principle of respect for autonomous agents that, we reckon, can address many of the concerns driving recent scholarship on partnership and public participation, while avoiding some of the limitations these concept have in the context of precision medicine. Our approach offers a normative clarification to how becoming partners in precision is compatible with retaining autonomy.Realigning the value of autonomy with ideals of direct engagement, we show, can provide adequate normative orientation to precision medicine; it can do justice to the idea of moral pluralism by stressing the value of moral self-determination: and, finally, it can reconcile the notion of autonomy with other more communitarian values such as participation and solidarity.

Health-related research is increasingly drawing on novel sources of online data, such as crowdsourced information about disease outbreaks, consumer-supplied information provided to health or wellness Web sites, Internet search queries about personal health, and social network postings that identify health behaviors. We offer examples of online sources and their uses, identify ethical and policy issues they generate, and formulate key questions for future discussion and investigation. Further work in this area will require cross-disciplinary collaboration to develop ethics and policy guidance for the ethical use of these novel data sources in health-related research.

Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices. [ABSTRACT FROM AUTHOR]