The Internet of Things (IoT) is, in part, an information handling system that can remove humans from the information handling process. The particular problem explored is how we are to understand privacy when considering informational systems that handle personal information in ways that impact people’s lives when there is no human operator in direct contact with that personal information. I argue that these new technologies need to take concepts like privacy into account, but also, that we ought also to take (...) these technologies into account to reconsider and perhaps reconceptualise privacy. This paper argues that while an inhuman system like the IoT does not necessarily violate the interpersonal privacy of people, if the IoT is used as part of a state surveillance program, a political notion of privacy may be violated. (shrink)

In February 2012, the Obama White House endorsed a Privacy Bill of Rights, comprising seven principles. The third, “Respect for Context,” is explained as the expectation that “companies will collect, use, and disclose personal data in ways that are consistent with the context in which consumers provide the data.” One can anticipate the contested interpretations of this principle as parties representing diverse interests vie to make theirs the authoritative one. In the paper I will discuss three possibilities and explain why (...) each does not take us far beyond the status quo, which, regulators in the United States, Europe, and beyond have found problematic. I will argue that contextual integrity offers the best way forward for protecting privacy in a world where information increasingly mediates our significant activities and relationships. Although an important goal is to influence policy, this paper aims less to stipulate explicit rules than to present an underlying justificatory, or normative rationale. Along the way, it will review key ideas in the theory of contextual integrity, its differences from existing approaches, and its harmony with basic intuition about information sharing practices and norms. (shrink)

This article is about the role of factual uncertainty for moral decision-making as it concerns the ethics of machine decision-making. The view that is defended here is that factual uncertainties require a normative evaluation and that ethics of machine decision faces a triple-edged problem, which concerns what a machine ought to do, given its technical constraints, what decisional uncertainty is acceptable, and what trade-offs are acceptable to decrease the decisional uncertainty.

Due to the technology of machine learning, new breakthroughs are currently being achieved with constant regularity. By using machine learning techniques, computer applications can be developed and used to solve tasks that have hitherto been assumed not to be solvable by computers. If these achievements consider applications that collect and process personal data, this is typically perceived as a threat to information privacy. This paper aims to discuss applications from both fields of personality and image analysis. These applications are often (...) criticized by reference to the protection of privacy. This paper critically questions this approach. Instead of solely using the concept of privacy to address the risks of machine learning, it is increasingly necessary to consider and implement ethical anti-discrimination concepts, too. In many ways, informational privacy requires individual information control. However, not least because of machine learning technologies, information control has become obsolete. Hence, societies need stronger anti-discrimination tenets to counteract the risks of machine learning. (shrink)