Algorithmic States of Exception

Article English OPEN
McQuillan, Daniel (2015)
  • Publisher: SAGE Publications

In this paper I argue that pervasive tracking and data-mining are leading to shifts in governmentality that can be characterised as algorithmic states of exception. I also argue that the apparatus that performs this change owes as much to everyday business models as it does to mass surveillance. I look at technical changes at the level of data structures, such as the move to NoSQL databases, and how this combines with data-mining and machine learning to accelerate the use of prediction as a form of governance. The consequent confusion between correlation and causation leads, I assert, to the creation of states of exception. I set out what I mean by states of exception using the ideas of Giorgio Agamben, focusing on the aspects most relevant to algorithmic regulation: force-of and topology. I argue that the effects of these states of exception escape legal constraints such as concepts of privacy. Having characterised this as a potentially totalising change and an erosion of civil liberties, I ask in what ways the states of exception might be opposed. I follow Agamben by drawing on Walter Benjamin's concept of pure means as a tactic that is itself outside the frame of law-producing or law-preserving activity. However, the urgent need to respond requires more than a philosophical stance, and I examine two examples of historical resistance that satisfy Benjamin's criteria. For each in turn I draw connections to contemporary cases of digital dissent that exhibit some of the same characteristics. I conclude that it is possible both theoretically and practically to resist the coming states of exception and I end by warning what is at stake if we do not.
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