In this paper, we defend what we call the ‘Hybrid View’ of privacy. According to this view, an individual has privacy if, and only if, no one else forms an epistemically warranted belief about the individual’s personal matters, nor perceives them. We contrast the Hybrid View with what seems to be the most common view of what it means to access someone’s personal matters, namely the Belief-Based View. We offer a range of examples that demonstrate why the Hybrid View is (...) more plausible than the Belief-Based View. Finally, we show how the Hybrid View generates a more plausible fit between the concept of privacy, and the concept of a (morally objectionable) violation of privacy. (shrink)

Often, when we share information about ourselves, we contribute to people learning personal things about others. This may happen because what we share about ourselves can be used to infer personal information about others. Such dependencies have become known as privacy dependencies in the literature. It is sometimes claimed that the scope of the right to privacy should be expanded in light of such dependencies. For example, some have argued that inferring information about others can violate their right to privacy. (...) Others have argued that sharing personal information about yourself that license such inferences can by itself violate the right to privacy. In this paper, we argue that the latter view should be rejected. (shrink)

In this paper, I defend what I call the ‘Inference Principle’. This principle holds that if an agent obtains some information legitimately, then the agent can make any inference she wants based on the information, without violating anyone’s right to privacy. This principle is interesting for at least three reasons. First, it constitutes a novel answer to the timely question of whether the widespread use of ‘data analytics’ to infer personal information about individuals is morally permissible. Second, it contradicts what (...) seems to be a common view of inferences’ ability to violate privacy rights. Third, it offers an account of the theoretically underdeveloped issue of what duties are engendered by the moral right to privacy with regard to inferred information. (shrink)

Luciano Floridi was not the first to discuss the idea of group privacy, but he was perhaps the first to discuss it in relation to the insights derived from big data analytics. He has argued that it is important to investigate the possibility that groups have rights to privacy that are not reducible to the privacy of individuals forming such groups. In this paper, we introduce a distinction between two concepts of group privacy. The first, the “what happens in Vegas (...) stays in Vegas” privacy (in the following: WHVSV privacy), deals with confidential information shared with the member of a group and inaccessible to (all or a specific group of) outsiders. The second, to which we shall refer as inferential privacy, deals with the inferences that can be made about a group of people defined by a feature, or combination thereof, shared by all individuals in the group. We show why we unreservedly agree with Floridi that groups can have a form of privacy that amounts to more than the mere fact of being sets of individuals each of whom has individual privacy; moreover, like Floridi, we find it plausible that at least some groups (those satisfying our definition of type-a groups) may have a right to a species of group privacy (that is, WHVSV privacy) as groups (and not just as individuals who belong to those groups). However, by turning our attention to the context of big data analytics, we show that the relevant, new notion of group privacy is one of inferential privacy. We argue that an absolute right (either of individuals or groups) to inferential privacy is implausible. We also show that many groups generated algorithmically (those satisfying our definition of type-b groups) cannot be right holders as groups (unless they become type-a groups). (shrink)

Privacy rights can surely be waived. But can they also be forfeited? If so, why and under what conditions? This article takes up these questions by developing a novel theory of privacy rights forfeiture that draws inspiration from Judith Thomson’s canonical work on privacy. The paper identifies two species of forfeiture rooted in modes of negligent and reckless conduct and argues that both self-directed and other-regarding considerations play a role in grounding forfeiture. The paper also contributes to the literature by (...) proposing a new taxonomy of how Hohfeldian entitlements can be divested generally. It likewise engages at length with a recent article by Benedict Rumbold and James Wilson in which the authors criticize Thomson’s work and appear to reject the very idea of privacy forfeiture. In response, I make the case that Rumbold and Wilson’s forfeiture-free model of privacy is unconvincing on substantive moral grounds. (shrink)

Journal of Political Philosophy, EarlyView.

Do privacy rights restrict what is permissible to infer about others based on statistical evidence? This paper replies affirmatively by defending the following symmetry: there is not necessarily a morally relevant difference between directly appropriating people’s private information—say, by using an X-ray device on their private safes—and using predictive technologies to infer the same content, at least in cases where the evidence has a roughly similar probative value. This conclusion is of theoretical interest because a comprehensive justification of the thought (...) that statistical inferences can violate privacy rights is lacking in the current literature. Secondly, the conclusion is of practical interest due to the need for moral assessment of emerging predictive algorithms. (shrink)