The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue (...) would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced. (shrink)

This paper examines the consequences of a culture of “personal ethics” when using new methodologies, such as the use of social media sites as a source of data for research. Using SM research as an example, this paper explores the practices of a number of actors and researchers within the “Ethics Ecosystem” which as a network governs ethically responsible research behaviour. In the case of SM research, the ethical use of this data is currently in dispute, as even though it (...) is seemingly publically available, concerns relating to privacy, vulnerability, potential harm and consent blur the lines of responsible ethical research behaviour. The findings point to the dominance of a personal, bottom-up, researcher-led, ‘ethical barometer’ for making decisions regarding the permissibility of using SM data. We show that the use of different barometers by different researchers can lead to wide disparities in ethical practice - disparities which are compounded by the lack of firm guidelines for responsible practice of SM research. This has widespread consequences on the development of shared norms and understandings at all levels, and by all actors within the Ethics Ecosystem, and risks inconsistencies in their approaches to ethical decision-making. This paper argues that this governance of ethical behaviour by individual researchers perpetuates a negative cycle of academic practice that is dependent on subjective judgements by researchers themselves, rather than governed by more formalised academic institutions such as the research ethics committee and funding council guidelines. (shrink)

Medical artificial intelligence (AI) is considered to be one of the most important assets for the future of innovative individual and public health care. To develop innovative medical AI, it is necessary to repurpose data that are primarily generated in and for the health care context. Usually, health data can only be put to a secondary use if data subjects provide their informed consent (IC). This regulation, however, is believed to slow down or even prevent vital medical research, including AI (...) development. For this reason, a number of scholars advocate a moral civic duty to share electronic health records (EHRs) that overrides IC requirements in certain contexts. In the medical AI context, the common arguments for such a duty have not been subjected to a comprehensive challenge. This article sheds light on the correlation between two normative discourses concerning informed consent for secondary health record use and the development and use of medical AI. There are three main arguments in favour of a civic duty to support certain developments in medical AI by sharing EHRs: the ‘rule to rescue argument’, the ‘low risks, high benefits argument’, and the ‘property rights argument’. This article critiques all three arguments because they either derive a civic duty from premises that do not apply to the medical AI context, or they rely on inappropriate analogies, or they ignore significant risks entailed by the EHR sharing process and the use of medical AI. Given this result, the article proposes an alternative civic responsibility approach that can attribute different responsibilities to different social groups and individuals and that can contextualise those responsibilities for the purpose of medical AI development. (shrink)