In recent years, neuroscience has been making dramatic progress. The discipline holds great promise but also raises a number of important ethical concerns. Among these is the concern that, some day in the distant future, we will have brain scanners capable of reading our minds, thus making our inner thoughts transparent to others. There are at least two reasons why we might regret our resulting loss of privacy. One is, so the argument goes, that this would undermine our ability to (...) form intimate relations. Another is that the omnipresent gaze of others would render an authentic inner life impossible. I argue that both of these concerns are exaggerated. First, intimacy might flourish through the differential acknowledgement of knowledge as common knowledge; for example, even if I know that both a friend and my taxi driver know that I have kinky sexual fantasies, I might only acknowledge this as common knowledge and, thus, an admissible piece of conversation with my friend, and this differential acknowledgement might be enforced by norms of social interaction. Second, the gaze of others would become much less oppressive if everyone’s inner lives were transparent to everyone else. I also argue that our minds are already partly transparent to others through the use of non-neuroscience-assisted mindreading techniques and, thus, that the latter offer no distinct threat to mind privacy. I offer an additional argument for this conclusion; to wit, that our minds extend beyond our brains. (shrink)

The rule of rescue describes the moral impulse to save identifiable lives in immediate danger at any expense. Think of the extremes taken to rescue a small child who has fallen down a well, a woman pinned beneath the rubble of an earthquake, or a submarine crew trapped on the ocean floor. No effort is deemed too great. Yet should this same moral instinct to rescue, regardless of cost, be applied in the emergency room, the hospital, or the community clinic? (...) -/- In health care, the desire to save lives at any cost must be reconciled with the reality of resource scarcity. As one example, the estimated cost for prophylactic Factor VIII to treat one patient with hemophilia for one year is $300,000. Costs of this magnitude have been accepted by public and private insurers in the developed world, even though, in principle, these sums could provide greater overall health benefit if allocated to pay for the unmet health care needs of many other patients. Looking forward, however, broad application of the rule of rescue will be increasingly untenable. But the moral instinct will remain: the desire to help those weakest among us, especially when their small numbers allow us to see them as unique individuals. What, then, is the ethical framework that can guide coverage and reimbursement decisions for orphan drugs into the future? (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)