The algorithmic other: Dehumanization, infrastructure, and racial capitalism in the age of AI

This article develops the concept of the algorithmic other to explain how artificial intelligence systems operationalize structural dehumanization through infrastructures of classification. Moving beyond models of bias, fairness, or representation, the article argues that algorithmic governance produces differential personhood by translating historically constituted social hierarchies into computational logics of legibility, prediction, and control. Drawing on Black feminist science studies, racial capitalism, Indigenous data sovereignty, and crip theory, the article reconceptualizes dehumanization not as an affective failure or design flaw but as an infrastructural condition embedded within data capitalism. Using a critical theoretical synthesis and comparative case analysis of facial recognition, predictive policing, and credit scoring, the article identifies three interlocking mechanisms—misclassification, hypervisibility, and erasure—through which algorithmic systems fabricate and manage social difference. These mechanisms operate relationally: misclassification produces predictive illegibility; hypervisibility territorializes exposure as governance; and erasure structures differential inclusion through systems of ranking, visibility, and exclusion. Together, they constitute a form of computational hierarchy that organizes recognition, risk, and disposability at scale. By theorizing dehumanization as procedural and infrastructural rather than psychological or interpersonal, the framework extends existing approaches to algorithmic bias, surveillance capitalism, and data colonialism. The article concludes by outlining a research agenda for studying algorithmic power through ethnography, transnational comparison, and participatory design while foregrounding the need to reconstruct data infrastructures toward epistemic justice rather than merely reform their outputs.