Personal information is key to informational privacy and the algorithmically generated profiles of individuals. However, the concept of personal information and its nature is rarely discussed. The concept of personal information thus seems to be based on an idea of information as objective and truthful—as natural information—that is depicted as digital footprints in the online and digital realm. I argue that the concept of personal information should exit the realm of natural information and enter the realm of nonnatural information—grounded in (...) meaning, intention, and convention—as this will provide us with a concept that can account for potential misleadingness, inaccuracies, and mistakes. (shrink)

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 a (morally objectionable) violation of privacy. (shrink)

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)

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)

In this paper, I offer an indirect argument for the Access Theory of privacy. First, I develop a new version of the rival Control Theory that is immune to all the classic objections against it. Second, I show that this new version of the Control Theory collapses into the Access Theory. I call the new version the ‘Negative Control Account’. Roughly speaking, the classic Control Theory holds that you have privacy if, and only if, you can control whether other people (...) know personal information about you. Critics of the Control Theory often give counterexamples, where privacy is either not diminished even though the claimant has lost control, or where privacy is diminished even though the claimant is in control. I argue that none of these alleged counterexamples work against the Negative Control Account. However, this is not a victory for the control theorist, because the Negative Control Account collapses into the Access Theory. The paper thus adds to the recent trend in the literature of favoring the Access Theory over the Control Theory. (shrink)

In the literature on conspiracy theories, the least contentious part of the academic discourse would appear to be what we mean by a “conspiracy”: a secretive plot between two or more people toward some end. Yet what, exactly, is the connection between something being a conspiracy and it being secret? Is it possible to conspire without also engaging in secretive behavior? To dissect the role of secrecy in con- spiracies – and thus contribute to the larger debate on the epistemology (...) of conspir- acy theories – we dene the concepts of “conspiracy,” “conspirator,” and “secret,” and argue that while conspirators might typically be thought to commit to keeping secrets once their conspiracy is underway, the idea that conspiracies are necessarily secretive to start with is not as obvious as previously thought. (shrink)

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment (...) and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)