Over the past decade Identity Management has become a central theme in information technology, policy, and administration in the public and private sectors. In these contexts the term ‘Identity Management’ is used primarily to refer to ways and methods of dealing with registration and authorization issues regarding persons in organizational and service-oriented domains. Especially due to the growing range of choices and options for, and the enhanced autonomy and rights of, employees, citizens, and customers, there is a growing demand for (...) systems that enable the regulation of rights, duties, responsibilities, entitlements and access of innumerable people simultaneously. ‘Identity Management’ or ‘Identity Management Systems’ have become important headings under which such systems are designed and implemented. But there is another meaning of the term ‘identity management’, which is clearly related and which has gained currency. This second construal refers to the need to manage our moral identities and our identity related information. This paper explores the relation between the management of our (moral) identities and ‘Identity Management’ as conceptualized in IT discourse. (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.

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)

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)