This chapter provides a high-level overview of key ethical issues raised by the use of surveillance technologies, such as digital contact tracing, disease surveillance, and vaccine passports, to combat the COVID-19 pandemic. To some extent, these issues are entirely familiar. I argue that they raise old questions in new form and with new urgency, at the intersection of information ethics, research ethics, and public health. Whenever we deal with data-driven technologies, we have to ask how they fare in relation to (...) important values like privacy, fairness, transparency, and accountability—values emphasized by information ethics scholars. Likewise, when such technologies put individuals at risk in order to drive scientific research and knowledge construction, we have to ask how they implicate values such as autonomy, beneficence and non-maleficence, and justice—values central to research ethics. And as researchers focusing on health information have long argued, when the data collected by these technologies pertain to individuals and public health, these ethical issues take on a special cast. -/- It is also true, however, that the pandemic has placed these questions in a new and revealing light. I highlight three insights from information ethics and research ethics that can help us navigate this difficult terrain. First, the value of privacy is instrumental, not absolute—there is nothing wrong with asking how to balance privacy against other important values. Second, privacy has both individual and social importance. Weighing privacy, on one hand, and public health, on the other, is not, therefore, a contest between individual and collective interests. Rather, it is an attempt to balance disparate public goods. Third, we ought to put these kinds of ethical decisions in the hands of third parties, rather than leaving them up to those who directly stand to benefit from them. In the case of pandemic surveillance technologies, this should mean more public, democratic oversight. (shrink)

Differential privacy (DP) aims to confer data processing systems with inherent privacy guarantees, offering strong protections for personal data. But DP’s approach to privacy carries with it certain assumptions about how mathematical abstractions will be translated into real-world systems, which—if left unexamined and unrealized in practice—could function to shield data collectors from liability and criticism, rather than substantively protect data subjects from privacy harms. This article investigates these assumptions and discusses their implications for using DP to govern data-driven systems. In (...) Parts 1 and 2, we introduce DP as, on one hand, a mathematical framework and, on the other hand, a kind of real-world sociotechnical system, using a hypothetical case study to illustrate how the two can diverge. In Parts 3 and 4, we discuss the way DP frames privacy loss, data processing interventions, and data subject participation, arguing it could exacerbate existing problems in privacy regulation. In part 5, we conclude with a discussion of DP’s potential interactions with the endogeneity of privacy law, and we propose principles for best governing DP systems. In making such assumptions and their consequences explicit, we hope to help DP succeed at realizing its promise for better substantive privacy protections. (shrink)

If I decide to disclose information about myself, this act can undermine other people’s ability to effectively conceal information about themselves. One case in point involves genetic information: if I share ‘my’ genetic information with others, I thereby also reveal genetic information about my biological relatives. Such dependencies are well-known in the privacy literature and are often referred to as ‘privacy dependencies’. Some take the existence of privacy dependencies to generate a moral duty to sometimes avoid sharing information about oneself. (...) If true, we argue, then it is sometimes justified for others to impose harm on the person sharing the information to prevent them from doing so. This is a highly revisionary implication. Hence, one must either endorse a highly revisionary view on what one may do to protect one’s privacy, or one must reject the view that privacy dependencies can be used to justify a moral duty that constrains choices about sharing information about oneself. (shrink)

In this paper, I approach the power of digital platforms by using the republican concept of domination. More specifically, I argue that the traditional, agent-relative interpretation of domination, in the case of digital domination, is best supplemented by a more radical version, on which republicans ought to give priority to structural elements. I show how radical republicanism draws attention to (1) the economic rationales and the socio-technical infrastructures that underlie and support digital platforms and to (2) the forms of influence (...) that are directed at cognitive dimensions, such as habituation and routinisation, which are particularly relevant for the power of digital platforms. These insights also imply that republicans have reason to favour a more structural response to digital platforms over more direct and individualised forms of control that fit with a ‘standard’ republican approach, such as securing exit options and requiring user consent. (shrink)