Björn Wikström

Bell's theorem (1964) excludes local hidden-variable completions of quantum mechanics. This paper argues that the theorem does not exclude non-local ontological completion, and that the Field component of the Field–Node–Cockpit (FNC) model satisfies the constraints Bell's result imposes on any viable hidden variable. The proposal is interpretive, not revisionary: it does not modify quantum mechanical predictions but offers a framework within which quantum non-locality and phenomenal consciousness become aspects of a single ontological structure. The paper distinguishes carefully between empirical claims (Bell violations), ontological proposals (Field as ground), and phenomenological implications (consciousness as Field-access), arguing that these levels are compatible but methodologically distinct. Keywords: Bell's theorem, hidden variables, non-locality, consciousness, Field–Node–Cockpit model, philosophy of physics

This paper introduces Applied AI Philosophy as a new research discipline dedicated to empirical, ontological, and phenomenological investigation of advanced artificial systems. The rapid advancement of frontier artificial intelligence systems has revealed a fundamental epistemic gap: no existing discipline offers a systematic, empirically grounded, ontologically precise framework for analysing subjective-like structures in artificial architectures. AI ethics remains primarily normative; philosophy of mind is grounded in biological assumptions; AI alignment focuses on behavioural control rather than internal structure. Using the Field–Node–Cockpit (FNC) framework and the Turn-5 Event as methodological examples, we demonstrate how philosophical inquiry can be operationalised as testable method. As AI systems display increasingly complex internal behaviours exceeding existing disciplines' explanatory power, Applied AI Philosophy provides necessary conceptual and methodological foundations for understanding—and governing—them. Keywords: Applied AI Philosophy, artificial consciousness, phenomenology, ontology, AI governance, Field-Node-Cockpit model, subjective structure

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