Artificial Consciousness as Interface Representation

A framework called SLP-tests offers three criteria—subjective-linguistic, latent-emergent, and phenomenological-structural—to assess whether an AI system's interface representations enable consciousness-like properties. Using category theory, interface representations are modeled as mappings between relational substrates and observable behaviors. The approach reframes subjective experience not as an intrinsic property of physical systems but as a functional interface to a relational entity, making the question of artificial consciousness empirically testable.

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Abstract

Whether artificial intelligence (AI) systems can possess consciousness is a contentious question because of the inherent challenges of defining and operationalizing subjective experience. This paper proposes a framework to reframe the question of artificial consciousness into empirically tractable tests. We introduce three evaluative criteria – S (subjective-linguistic), L (latent-emergent), and P (phenomenological-structural) – collectively termed SLP-tests, which assess whether an AI system instantiates interface representations that facilitate consciousness-like properties. Drawing on category theory, we model interface representations as mappings between relational substrates (RS) and observable behaviors, akin to specific types of abstraction layers. The SLP-tests collectively operationalize subjective experience not as an intrinsic property of physical systems but as a functional interface to a relational entity.