Human Hypersubstitution Theory: Critical Rehumanization in Algorithmic Ecosystems
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The rise of autonomous algorithmic systems has been reconfiguring the ontological, epistemological, and relational foundations of the human condition. Confronted with the social and ethical gap of the “erosion of human agency” and the urgency of a perspective of “society shaping the algorithms,” actively rather than passively, we still lack integrated frameworks that articulate trust, empathy, ethics, biases, and autonomy in diagnosis, in sociotechnical structure, and in institutional implications. This study develops such a model, presenting Human Hypersubstitution Theory as a novel conceptual proposal, systematically named, structured, and here formalized for the first time. It analyzes the effects of algorithmic automation on subjectivity, language, and social practices, offering directions for confronting the algorithmic situation. The theory is anchored in a genealogy that comprises Anders’s philosophy of technology (erosion of agency and responsibility), Baudrillard’s critique of simulation (implosion of the real and dissolution of the Other), and McLuhan’s media theory (sensory environments, hyperextension, and the global village). It dialogues with contemporary critical perspectives and is structured around three axes of response: planetary ethical governance, critical and algorithmic literacy, and institutional practices of rehumanization. Empirical evidence in fields such as education, health, and culture supports its propositions, revealing unprecedented forms of alienation—simulated bonds, parameterized subjectivities, and automated decisions as the norm. The notion of an Algorithmic Nebula, designed on fuzzy logic, models the transition from personalization to personification. The study concludes that Human Hypersubstitution Theory provides foundations for ethical, political, and critical-literacy interventions, calling for sociotechnical governance capable of reclaiming autonomy, empathy, and responsibility in an AI-mediated future.