Hannah Lim

The infection rates of COVID-19 have been exponential in some countries despite the imposition of infectious disease control measures such as lockdowns and physical distancing, which form one of the basic principles of public health and infectious disease control. There have been significant problems with leaders and citizenry deliberately ignoring and not complying with such measures and which have directly resulted in sudden rises in infection numbers. Here, I show the nature and extent of the widespread problem and argue that the problem is in large part due to our modern society characterised by liberal individualism. I apply the philosophy proposed by philosopher Alasdair MacIntrye to show that one key underlying cause of the non-compliant behaviour of citizenry is due to modern liberal individualism that has deprived the modern nation state of the opportunities and authority for it to teach or to dictate what is the common good of the society as a whole to individuals in its community. This is the first time MacIntyre’s philosophy has been applied to public health, and this paper demonstrates the need for ethics education to counter-balance liberal individualism in order to contain and to prevent another pandemic and public health crisis in modern society.

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 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, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data.

Artificial intelligence is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research. The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper.