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Pseudo-Consciousness in Artificial Intelligence: a functional and governance framework for consciousness-like systems

José Augusto de Lima Prestes

PhilSci-Archive April 5, 2026 Peer reviewed DOI: 10.5281/zenodo.19430961 via OpenAlex

Summary

Advanced AI systems that perform many functions associated with consciousness—integrating information, revising outputs, transferring skills across domains, simulating goals, and maintaining a consistent behavioral profile—should be understood through a new category called 'pseudo-consciousness,' rather than being attributed inner subjective experience. This concept occupies the middle ground between reactive automation and genuinely conscious agents. Pseudo-consciousness provides a disciplined vocabulary for systems whose social effects exceed older categories like narrow AI, and clarifies why they generate distinctive ethical and governance concerns even without evidence for consciousness. The article develops five conditions for identifying pseudo-conscious profiles and examines boundary cases involving large language models, multimodal systems, and tool-using agents.

Study at a glance

Design theoretical or philosophical paper
Key finding Pseudo-consciousness is a theoretically serious and practically necessary framework for interpreting AI systems that perform the external grammar of mindedness under persistent uncertainty about their inner status.

Abstract

This article develops "pseudo-consciousness" as an analytical category for advanced artificial intelligence systems whose organized performance of consciousness-associated functions reshapes how they are interpreted, trusted, and governed without thereby justifying a positive attribution of phenomenal subjectivity. The central claim is not that machine consciousness is impossible in principle, but that current debate requires a concept for the increasingly important middle terrain between reactive automation and genuinely conscious agents. Many contemporary systems integrate heterogeneous information, revise their own outputs, transfer competencies across domains, simulate goal-directed organization, and sustain a recognizable behavioral profile across contexts, while remaining more plausibly understood, on present evidence, through a functional and governance-oriented lens than through an attribution of inner experience. The article situates this proposal within recent debates in the science and philosophy of consciousness, including theory-sensitive approaches to AI consciousness assessment, disputes between computational and biologically grounded views, and emerging empirical work on self-reference, introspection-like reports, trust, and moral attribution in large language models. It argues that pseudo-consciousness is useful because it identifies a non-trivial configuration of capacities associated with the appearance of mindedness, provides a disciplined vocabulary for systems whose social effects exceed older categories such as "narrow AI," and clarifies why such systems generate distinctive ethical and governance concerns even in the absence of defensible evidence for consciousness. The paper develops five task-sensitive conditions for identifying pseudo-conscious profiles---global information integration, recursive metacognitive correction, cross-domain transfer competence, intentionality simulation without subjectivity, and behavioral coherence across domains---and uses them to examine boundary cases involving large language models, multimodal systems, and tool-using agents. It then shows how such profiles acquire social force through anthropomorphic uptake, relational asymmetry, and institutionally salient forms of trust, before turning to their ethical and governance implications. The conclusion is that pseudo-consciousness should be understood neither as a synonym for consciousness nor as a mere metaphor, but as a theoretically serious and practically necessary framework for interpreting systems that perform the external grammar of mindedness under persistent uncertainty about their inner status. Keywords: pseudo-consciousness, large language models, anthropomorphism, social attribution, human-AI interaction, AI governance. Preprint Version 3 (April 5, 2026). This version supersedes the author's earlier preprint versions (v1 and v2) and reflects both the maturation of the underlying argument and the subsequent evolution of the relevant literature. It includes conceptual reformulations, updated engagement with recent debates, and a more explicit functional and governance-oriented framing of pseudo-consciousness and related issues. The manuscript has been submitted to a journal and is currently under peer review. It may therefore be revised further following editorial assessment and peer review. Readers are advised to consult the most recent version for citation and interpretive purposes.

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