Initial results of the Digital Consciousness Model

The evidence against large language models (LLMs) from 2024 being conscious is not decisive, though it is stronger than the evidence against consciousness in simpler AI systems. The Digital Consciousness Model (DCM) provides a systematic, probabilistic framework for assessing consciousness in AI, incorporating multiple leading theories rather than a single one. It allows comparison across different AIs and biological organisms and tracks how evidence evolves as AI develops. The DCM's initial results show that while current LLMs likely lack consciousness, the case against them is far from settled.

Study at a glance

Abstract

Artificially intelligent systems have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that surprise even their creators. This raises a crucial question: Are we creating systems that are conscious? The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way. It provides a shared framework for comparing different AIs and biological organisms, and for tracking how the evidence changes over time as AI develops. Instead of adopting a single theory of consciousness, it incorporates a range of leading theories and perspectives - acknowledging that experts disagree fundamentally about what consciousness is and what conditions are necessary for it. This report describes the structure and initial results of the Digital Consciousness Model. Overall, we find that the evidence is against 2024 LLMs being conscious, but the evidence against 2024 LLMs being conscious is not decisive. The evidence against LLM consciousness is much weaker than the evidence against consciousness in simpler AI systems.