Informational self meaning as a structural theory in artificial intelligence ontology and epistemology
TL;DR
This paper introduces the concept of informational self-meaning (ISM) as a philosophical account for addressing AI ontology and epistemology, providing a conceptual foundation for gradualist models of AI recognition in legal and ethical contexts.
Abstract
This paper introduces the concept of informational self-meaning (ISM) as a philosophical account for addressing AI ontology and epistemology. Unlike theories such as Higher-Order Thought (HOT), Integrated Information Theory (IIT), and Global Workspace Theory (GWT), which encounter regress, reductionism, or anthropocentric limits, ISM characterises consciousness structurally as the capacity for rule-level modification rather than mere parameter adjustment. Drawing from both Western (Aristotle, Leibniz, Derrida, Floridi) and Eastern (Huayan, Yogācāra) traditions, the paper outlines three operational conditions—contextual coherence, emergent abstraction, and structural self-modification—and integrates them into a conceptually grounded and empirically testable framework. Empirical anchors from psycholinguistics [23, 26] and recent AI developments (GPT models, RLHF, meta-learning, Reflexion) demonstrate that ISM offers not only philosophical clarity but also practical verification protocols. Its contribution lies in bridging metaphysics, cognitive science, and AI ethics, providing a conceptual foundation for gradualist models of AI recognition in legal and ethical contexts.