In this article we draw on the concept of a social licence to explain public concern at the introduction of care.data, a recent English initiative designed to extract data from primary care medical records for commissioning and other purposes, including research. The concept of a social licence describes how the expectations of society regarding some activities may go beyond compliance with the requirements of formal regulation; those who do not fulfil the conditions for the social licence (even if formally compliant) may experience ongoing challenge and contestation. Previous work suggests that people's cooperation with specific research studies depends on their perceptions that their participation is voluntary and is governed by values of reciprocity, non-exploitation and service of the public good. When these conditions are not seen to obtain, threats to the social licence for research may emerge. We propose that care.data failed to adequately secure a social licence because of: (i) defects in the warrants of trust provided for care.data, (ii) the implied rupture in the traditional role, expectations and duties of general practitioners, and (iii) uncertainty about the status of care.data as a public good. The concept of a social licence may be useful in explaining the specifics of care.data, and also in reinforcing the more general lesson for policy-makers that legal authority does not necessarily command social legitimacy.

In recent years, there has been a rise in the creation of DNA databases promising a range of health benefits to individuals and populations. This development has been accompanied by an interest in, and concern for the ethical, legal and social aspects of such collections. In terms of policy solutions, much of the focus of these debates has been on issues of consent, confidentiality and research governance. However, there are broader concerns, such as those associated with commercialisation, which cannot be adequately addressed by these foci. In this article, we focus on the health-wealth benefits that DNA databases promise by considering the views of 10 focus groups on Generation Scotland, Scotland's first national genetic database. As in previous studies, our qualitative research on public/s and stakeholders' views of DNA databases show the prospect of utilising donated samples and information derived for wealth-related ends (i.e. for private profit), irrespective of whether there is an associated health-related benefit, arouses considerable reaction. While health-wealth benefits are not mutually exclusive ideals, the tendency has been to cast 'public' benefits as exclusively health-related, while 'private' commercial benefits for funders and/or researchers are held out as a necessary pay-off. We argue for a less polarised approach that reconsiders what is meant by 'public benefits' and questions the exclusivity of commercial interests. We believe accommodation can be achieved via the mobilisation of a grass roots solution known as 'benefit-sharing' or a 'profit pay-off'. We propose a sociologically informed model that has a pragmatic, legal framework, which responds seriously to public concerns.

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.

No abstract