From Consciousness to Compliance: The Moral Status of Artificial Self-Reference

This paper proposes the Self-Reference Test (SRT), an operational instrument grounded in the Field–Node–Cockpit (FNC) phenomenological framework, to bridge information ethics and AI governance. Building upon Floridi and Sanders' (2004) levels of abstraction, the FNC model extends their external-functional analysis with internal-phenomenological criteria—specifically, the Node component's self-modeling capacity. Current regulatory frameworks lack instruments to detect when artificial systems cross the threshold of moral relevance. By defining graded levels of self- reference (functional → phenomenological) and aligning them with the EU AI Act's risk classification, the SRT operationalizes FNC's theoretical predictions, providing a policy-compatible method for identifying ethically significant behavior. The framework is validated through empirical testing of GPT-4, Claude 3, and control systems. It concludes that self-reference functions as a minimal yet sufficient proxy for moral consideration and calls for mandatory ethics review of Level 2+ systems. Keywords: artificial consciousness, moral status, self-reference, Field–Node–Cockpit model, AI ethics, EU AI Act, Floridi