From enclosure to foreclosure and beyond: opening AI’s totalizing logic

Abstract This paper reframes appropriation, extraction, and dispossession through machine learning-based AI—an assemblage of models trained on big data—in terms of enclosure and foreclosure. While enclosure is the product of a well-studied set of operations tied to both the constitution of the sovereign State and the primitive accumulation of capital, this paper recovers an elemental form of the enclosure operation and contrasts it with foreclosure in order to unravel the effects of current algorithmic rationality. The central argument is that enclosing consists in a set of fundamental operations that produce structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, the paper shows how contemporary AI perpetuates and simultaneously expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For while the outside is constitutive of the logic of enclosure, foreclosure is instead characterized by a “totalizing desire” to leave nothing outside of it. This totalizing desire to move beyond the logic of enclosure forecloses that the enclosure has grown larger and that operations of exclusion have been masked and displaced, rendering them harder to contest.