Rapid advancements in machine learning techniques allow mass surveillance to be applied on larger scales and utilize more and more personal data. These developments demand reconsideration of the privacy-security dilemma, which describes the tradeoffs between national security interests and individual privacy concerns. By investigating mass surveillance techniques that use bulk data collection and machine learning algorithms, we show why these methods are unlikely to pinpoint terrorists in order to prevent attacks. The diverse characteristics of terrorist attacks—especially when considering lone-wolf terrorism—lead (...) to irregular and isolated footprints. The irregularity of data affects the accuracy of machine learning algorithms and the mass surveillance that depends on them which can be explained by three kinds of known problems encountered in machine learning theory: class imbalance, the curse of dimensionality, and spurious correlations. Proponents of mass surveillance often invoke the distinction between collecting data and metadata, in which the latter is understood as a lesser breach of privacy. Their arguments commonly overlook the ambiguity in the definitions of data and metadata and ignore the ability of machine learning techniques to infer the former from the latter. Given the sparsity of datasets used for machine learning in counterterrorism and the privacy risks attendant with bulk data collection, policymakers and other relevant stakeholders should critically re-evaluate the likelihood of success of the algorithms and the collection of data on which they depend. (shrink)

This special issue of Science and Engineering Ethics is devoted to the ethical, societal and political implications of new and emerging technologies in the area of Human Security. Its aim is to address the wider implications of an altered security landscape. Specifically, and in accordance with SEE’s main area of interest, contributions to this special issue focus on those ethical considerations warranted by scientific and technological advances in the field of human security. This includes, but is not restricted to, issues (...) of privacy and data protection, control, trust, surveillance, authority, and freedom. Papers consider some of the ethical and societal challenges related to new and emerging technologies in the context of the Human Security Doctrine (HSD), as it has been initially formulated by the United Nations Commission on Human Security (UNCHS 2003). The HSD argues for a paradigm shift from understanding security based on tangible assets (e.g., national borders, goo .. (shrink)