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BackgroundEthics 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 textIn 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.ConclusionsWe 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.

There are intense discussions about the ethical and societal implications of biomedical engineering, but little data to suggest how scientists think about the ethics of their work. The aim of this study is to describe how scientists frame the ethics of their research, with a focus on the field of molecular systems engineering. Semi-structured qualitative interviews were conducted during 2021–2022, as part of a larger study. This analysis includes a broad question about how participants view ethics as related to their work, with follow up probes about the topics they consider most important. Interviews were transcribed, inductively coded by two researchers to consensus, and analyzed thematically. Twenty-four scientists participated in the study. Interviewees hold positions as professors, principal investigators, and senior staff researchers in universities or research institutes in the United States and Europe. Among those scientists who reported reflecting on ethical considerations in their work, many equated ethics with research ethics topics (e.g., safety, replicability), or with regulation and guidelines. Participants expressed the view that ethical issues are primarily relevant for clinical trials of bioengineered products, or for those working with animal or human subjects. Scientists described their research as "too early" or "not examining anything living" with regard to ethical reflection. Finally, many felt that ethics is seen as territory for experts and therefore beyond scientists' competencies. Molecular systems engineering scientists currently focus on regulatory aspects as the framework for their ethical analyses. They describe using a framework to define when life arises, as a means to determine when further ethical engagement is warranted. Further research is needed to investigate how scientists relate to the ethics of their scientific work, and build consensus around concepts of life, autonomous behavior, and physiological relevance of bioengineered systems. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Machine learning (ML) having highly transformative approaches to the automation of numerous clinical tasks. Topics include computer scientists being most concerned with demonstrating the superiority of ML-driven systems at performing classificatory or predictive tasks; and focusing on ensuring the safety and effectiveness of medical devices, software, and drugs before receiving market authorization.

This study reports on 13 semistructured in-depth interviews to qualitatively explore the experiences of individuals who publicly shared their direct-to-consumer genetic testing results on the platform openSNP. In particular, we focused on interviewees' understanding of privacy. Participants reported that the likelihood and the magnitude of privacy harms depend on gender, ethnicity, sexual orientation, the stigma associated with certain clinical conditions, the existence of adequate legislation, and the nature of national health care systems. Some participants expressed the view that those who enjoy higher socioeconomic status or are better protected by their country's legislation have a responsibility to share their genetic data. Our study shows that people who share their genetic data publicly online—far from being insensitive to privacy risks—have a complex understanding of the social, relational, and contextual nature of genetic privacy. [ABSTRACT FROM AUTHOR]

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features (adaptivity, flexibility, monitoring, responsiveness, reflexivity, and inclusiveness) and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the desired impact unless oversight activities by different stakeholders acquire a common substantive orientation. [ABSTRACT FROM AUTHOR]