This article examines the interplay between contemporary algorithmic security technology and the political theory of the state of exception. I argue that the exception, as both a political and a technological concept, provides a crucial way to understand the power operating through machine learning technologies used in the security apparatuses of the modern state. I highlight how algorithmic security technology, through its inherent technical properties, carries exceptions throughout its political and technological architecture. This leads me to engage with Theodor Adorno’s (...) negative dialectics to interrogate the question of ‘ground truth’ in machine learning. I conclude that most machine learning technology asserts identity between itself and bourgeois reality – and thus inherently reinforces and reproduces the relations of domination entailed in that image of the world. However, space still exists for machine learning to operate within spaces of political non-identity, or exceptions to the bourgeois totality, and aid in liberatory politics. (shrink)

Data increasingly drive our lives. Often presented as a new trajectory, the deep immersion of our lives in data has a history that is well over a century old. By revisiting the work of early pioneers of what would today be called data science, we can bring into view both assumptions that fund our data-driven moment as well as alternative relations to data. I here excavate insights by contrasting a seemingly unlikely pair of early data technologists, Francis Galton and W.E.B. (...) Du Bois. Galton, well known for his contributions to eugenics, was first and foremost a tinkering technician of measure. There are numerous domains of science over which Galtonian conceptions retain considerable influence, presumably without his pride in racial inequality. A more viable, because more egalitarian, alternative for the present can be found in the early data work of Du Bois. (shrink)