Data Privacy

AI-enabled predictive analytics is widely deployed in clinical care settings for healthcare monitoring, diagnostics and risk management. The technology may offer valuable insights into individual and population health patterns, trends and outcomes. Predictive analytics may, however, also tangibly affect individual patient privacy and the right thereto. On the one hand, predictive analytics may undermine a patient’s state of privacy by constructing or modifying their health identity independent of the patient themselves. On the other hand, the use of predictive analytics may (...) violate the patient’s right to privacy if the patient has no control over the use or output of the technology. These repercussions ultimately erode patient autonomy and agency. This paper discusses these implications in further detail, and proposes possible measures for their mitigation. They involve the incorporation in the AI systems of accuracy-enhancing statistical models and methods, more privacy-conscious institutional policies and practices, and effective choice for patients to accept or refuse diagnostics and treatment drawing on AI-enabled predictive analytics. (shrink)

An issue about the privacy of the clustered groups designed by algorithms arises when attempts are made to access certain pieces of information about those groups that would likely be used to harm them. Therefore, limitations must be imposed regarding accessing such information about clustered groups. In the discourse on group privacy, it is argued that the right to privacy of such groups should be recognised to respect group privacy, protecting clustered groups against discrimination. According to this viewpoint, this right (...) places a duty on others, for example, private companies, institutions, and governments, to refrain from accessing such information. To defend the idea that the right to privacy should be recognised for clustered groups, at least two requirements must be satisfied. First, clustered group privacy must be conceived of as either a collective good or a participatory good. Since these forms of good are of the type from which no member of a group can be excluded from benefiting, the right to them is defined as a group right. Second, there must be group interests on which to base a group right. Group interests can be either the interests of those members that are a result of their being in the group or the interests of the group as a whole that transcend the interests of its members. However, this paper argues that clustered group privacy cannot be conceived of as either a collective or a participatory good because it is possible for some individuals to be excluded from benefiting from it. Furthermore, due to the lack of awareness among individuals that they are members of a clustered group and the nature of a clustered group itself, such groups cannot have the group interests necessary to establish a group right. Hence, the group right to privacy cannot be recognised for these groups, implying that the group right cannot be considered a means to protect clustered groups against discrimination. Instead, this paper suggests that moral principles need to be articulated within an ethics of vulnerability to identify the moral obligations of protecting vulnerable clustered groups. The duty owed to the vulnerable should involve refraining from accessing certain information about clustered groups in specific contexts. This duty is not engendered by the right to privacy of such groups; it is the duty owed to the vulnerable. The findings highlight the need to articulate moral principles regarding privacy and data protection to protect clustered groups in contexts in which accessing information about them could constitute a reason for discriminatory targeting. (shrink)

This paper presents an account of the right to privacy that is inspired by classic control views on this right and recent developments in moral psychology. The core idea is that the right to privacy is the right that others not make personal information about us flow unless this flow is an expression of and does not conflict with our deep self. The nature of the deep self will be spelled out in terms of stable intrinsic desires. The paper argues (...) that this view has advantages over alternative accounts of the right to privacy, that it is extensionally adequate in interesting test cases, that there is a good reason to think that the right to privacy, thus understood, can be justified, and that this view helps identify what kind of information is protected by the right to privacy. (shrink)

Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations highlighting ethical, political, and governance issues the use (...) of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (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)