The Fair Priority Model offers a practical way to fulfill pledges to distribute vaccine fairly and equitably

The prevailing view is that participation in biomedical research is above and beyond the call of duty. While some commentators have offered reasons against this, we propose a novel public goods argument for an obligation to participate in biomedical research. Biomedical knowledge is a public good, available to any individual even if that individual does not contribute to it. Participation in research is a critical way to support that important public good. Consequently, we all have a duty to participate. The current social norm is that people participate only if they have a good reason to do so. The public goods argument implies that people should participate unless they have a good reason not to. Such a shift would be of great aid to the progress of biomedical research, eventually making our society significantly healthier and longer-lived.We defend the obligation view. We do so not to make a philosophical point, but to stimulate support for a major cultural shift in the way physicians, researchers, patients, and society at large think about participation in research. The Standard ViewAccording to the standard view, participation in research is akin to giving blood or donating to charity. [1][2][3][4][5] Participation that benefits society is supererogatory -while participants who put themselves at risk or inconvenience deserve praise, people who refuse to participate in research are not acting wrongly or unethically.

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Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AIassisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data.

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