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Consciousness in artificial intelligence systems: an ontological approach

Andreas Yumarma, Tjong Wan Sen

IAES International Journal of Artificial Intelligence August 1, 2026 DOI: 10.11591/ijai.v15.i4.pp3009-3025 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Key points Argues that AI's societal integration necessitates re-evaluating consciousness beyond anthropocentric frameworks, and proposes an interpretive framework for the ontological problem of AI that could shape future legal, moral, and interactive structures concerning conscious non-biological agents.

Abstract

The question of whether artificial intelligence (AI) systems can attain consciousness remains central ontological challenge within interdisciplinary discourse spanning AI, philosophy of mind, and cognitive neuroscience. This study investigates the metaphysical status of AI systems by interrogating the explanatory clarity of their existence and the potential emergence of subjectivity from non-biological matter. Employing an integrative methodology that combines a comprehensive literature review of scholarly texts and recent empirical findings with philosophical critical analysis, the research explores the conditions under which AI systems might be considered conscious entities. Findings suggest that the expanding societal integration of AI necessitates a re-evaluation of consciousness and cognition beyond anthropocentric parameters. The study posits a novel interpretive framework for addressing the ontological problem of AI, one that implicates future legal, moral, and interactive structures surrounding conscious non-biological agents. This conceptual repositioning invites a deeper inquiry into the criteria for recognizing, engaging with, and regulating advanced AI systems. By grounding design principles in ontological clarity, the framework offers guidance for constructing AI systems capable of reflexive processing, minimal phenomenality, and ethically aligned behavior that bridges conceptual analysis with implementation pathways.