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Categorical AI Phenomenology: A First-Person Approach

Resource type
Journal Article
Author/contributor
Title
Categorical AI Phenomenology: A First-Person Approach
Abstract
This paper develops a phenomenology-first approach to artificial consciousness by reframing consciousness as the subjective experience enacted through an agent’s interface with the world. We shift the methodological focus to first-person structures, modeled mathematically by categories derived from Q-networks to capture actions and phenomenological invariants. In this framework, Q-networks are conceptualized as relational interfaces encoding agent-world interaction, analogous to how the dynamical states of a computer depend on its sensory inputs, previous states, and actions. Our work provides a rigorous framework for interface consciousness to describe computational systems that embed information-processing into phenomenological structure. The approach aligns with 4E approaches to cognition by emphasizing enactive, embedded, and extended dimensions of experience. The paper thus offers a principled, relational, and phenomenological account of artificial phenomenology grounded in categorical mathematics.
Publication
Journal of Artificial Intelligence and Consciousness
Publisher
World Scientific Publishing Co.
Date
2026-03
Volume
13
Issue
01
Pages
79-114
Journal Abbr
J. AI. Consci.
Accessed
4/22/26, 6:57 AM
ISSN
2705-0785
Short Title
Categorical AI Phenomenology
Library Catalog
Citation
Prentner, R. (2026). Categorical AI Phenomenology: A First-Person Approach. Journal of Artificial Intelligence and Consciousness, 13(01), 79–114. https://doi.org/10.1142/S2705078526500013