Algorithmic sovereignty: Machine learning, ground truth, and the state of exception

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.

Automated summary

This article explores the interaction between contemporary algorithmic security technology and the political theory of the state of exception. It argues that algorithmic security technology carries exceptions throughout its political and technological architecture. The article concludes that while most machine learning technology reinforces and reproduces the relations of domination, there is still space for it to operate within spaces of political non-identity and support liberatory politics.