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

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, distinctions between extensional and intensional forms of artificial agency, proposals for testing machine consciousness, 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 useful framework for interpreting systems that perform the external grammar of mindedness under persistent uncertainty about their inner status.