Structural Logic of AI Surveillance and Its Normalisation in the Public Sphere

Abstract

This study examines the fundamental logics of surveillance impetus in the rapid transition to AI-based information processing. In this paper, these logics are called axioms—three principles of (1) concentrated architectural codes, (2) constrained user psychology, and (3) peculiar characteristics of data as information. This study argues that each axiom perpetuates AI's tendency to solidify data surveillance and normalises it in newly emerged AI-driven public spheres. This is a conceptual paper structured in the following sections—(a) axioms (three principles maintaining the impetus of surveillance normalisation), (b) mutual shaping (interaction between users and institutions reinforcing surveillance), and (c) policy remedies (policy principles fixing normalisation). The thesis of this paper is the normalisation of AI—perpetuated by three axioms—is the product of mutual shaping between institutions and uses as “data-hungry” algorithms exacerbate the tendency in which users are to participate willingly in surveillance. This poses the concern that data surveillance in its pronounced normalising processes becomes an industrial structural problem, not an episodic one. This paper concludes by calling for sanguine intervention measures, collectively tackling the structural recurrence of surveillance in the U.S.-specific contexts but also touching upon even broader global policy discussion.

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