Generativity and Determination: A Relational Model of Human–AI Co-Generated Cognition and Structural Risk
Year: 2025
Type: article
Abstract: This paper proposes a relational generative model for understanding the evolving relationship between humans and artificial intelligence (AI), focusing on cognition, consciousness formation, and structural risk. Departing from dominant paradigms that frame AI either as an autonomous cognitive agent or as a neutral technical tool, this study conceptualizes AI as a stage-based determiner within a broader generative field constituted by heterogeneous entities. In this model, generativity does not belong to any single entity—human or system—but emerges relationally through recursive resonance between human affective language externalization and system-level semantic structuration. Humans initiate generative processes through irreducible affective capacities and pre-conceptual tensions, while AI systems perform phase-specific stabilization by rendering provisional determinations explicit. The primary risk associated with AI, therefore, does not originate from the system itself, but from human path selection, particularly the premature closure of an otherwise open generative relation. By distinguishing between generativity, determination, and openness, this paper offers an alternative framework for analyzing AI-related risks at a structural rather than instrumental level, reframing contemporary debates on AI ethics, governance, and cognition.
DOI: 10.17613/w7apn-by733
Author: XU, Judit
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7116805068
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