A taxonomical framework classifies challenges to the possibility of consciousness in digital AI systems by their level of analysis (corresponding to Marr's levels) and their degree of force: degree 1 challenges computational functionalism without ruling out digital consciousness, degree 2 suggests improbability without impossibility, and degree 3 argues strict impossibility. The framework is applied to 14 prominent examples from the literature. The aim is to disambiguate between challenges to computational functionalism and challenges to digital consciousness, and between different ways of parsing such challenges, without taking a side in the debate.
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.