This paper aims to provide an overview of surveillance theories and concepts that can help to understand and debate surveillance in its many forms. As scholars from an increasingly wide range of disciplines are discussing surveillance, this literature review can offer much-needed common ground for the debate. We structure surveillance theory in three roughly chronological/thematic phases. The first two conceptualise surveillance through comprehensive theoretical frameworks which are elaborated in the third phase. The first phase, featuring Bentham and Foucault, offers architectural (...) theories of surveillance, where surveillance is often physical and spatial, involving centralised mechanisms of watching over subjects. Panoptic structures function as architectures of power, not only directly but also through disciplining of the watched subjects. The second phase offers infrastructural theories of surveillance, where surveillance is networked and relies primarily on digital rather than physical technologies. It involves distributed forms of watching over people, with increasing distance to the watched and often dealing with data doubles rather than physical persons. Deleuze, Haggerty and Ericson, and Zuboff develop different theoretical frameworks than panopticism to conceptualise the power play involved in networked surveillance. The third phase of scholarship refines, combines or extends the main conceptual frameworks developed earlier. Surveillance theory branches out to conceptualise surveillance through concepts such as dataveillance, access control, social sorting, peer-to-peer surveillance and resistance. With the datafication of society, surveillance combines the physical with the digital, government with corporate surveillance and top-down with self-surveillance. (shrink)

This paper is the result of the EMERGING ICT FOR CITIZEN VEILLANCE-workshop organized by the JRC, Ispra, Italy, March 2014. The aim of this paper is to explore how the subject participates in surveillance situations with a particular focus on how users experience everyday tracking technologies and practices. Its theoretical points of departure stem from Surveillance Studies in general and notions of participatory surveillance and empowering exhibitionism :199–215, 2004) in particular. We apply these theoretical notions on smartphones and its users (...) to investigate the combination of participation and surveillance. Empirically, the paper uses interviews held with urban nightlife visitors to uncover practices of smartphone use. This qualitative and explorative study contributes to the concept of participatory surveillance by discussing to what extent smartphone-users’ actions and motivations can be seen as forms of surveillance and how that influences these actors in a public space. We finish by setting out directions for studying mobile technologies of the self. (shrink)

In this article, we critique the ways in which the people involved in the development and application of AI systems often visualize and talk about AI systems. Often, they visualize such systems as shiny humanoid robots or as free-floating electronic brains. Such images convey misleading messages; as if AI works independently of people and can reason in ways superior to people. Instead, we propose to visualize AI systems as parts of larger, sociotechnical systems. Here, we can learn, for example, from (...) cybernetics. Similarly, we propose that the people involved in the design and deployment of an algorithm would need to extend their conversations beyond the four boxes of the _Error Matrix_, for example, to critically discuss _false positives_ and _false negatives_. We present two thought experiments, with one practical example in each. We propose to understand, visualize, and talk about AI systems in relation to a larger, complex reality; this is the requirement of _requisite variety_. We also propose to enable people from diverse disciplines to collaborate around _boundary objects_, for example: a drawing of an AI system in its sociotechnical context; or an ‘extended’ Error Matrix. Such interventions can promote meaningful human control, transparency, and fairness in the design and deployment of AI systems. (shrink)