Artificial Consciousness as Interface Representation
Artificial General Intelligence January 1, 2026 Peer reviewed DOI: 10.1007/978-3-032-00800-8_12 via Springer Nature
Summary
A framework called SLP-tests uses three criteria—subjective-linguistic, latent-emergent, and phenomenological-structural—to assess whether an AI system's interface representations enable consciousness-like properties. Category theory models these representations as mappings between relational substrates and observable behaviors. The approach treats subjective experience as a functional interface rather than an intrinsic property.
Study at a glance
| Design | proposed framework |
|---|---|
| Key finding | The SLP-tests operationalize subjective experience as a functional interface to a relational entity, making artificial consciousness empirically testable. |
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.