Beyond Computational Functionalism: The Behavioral Inference Principle for Machine Consciousness

The current consensus on attributing consciousness to large language models (LLMs) relies on computational functionalism, which ascribes consciousness based on computational equivalence. This opinion piece argues against that approach and proposes a behavioral inference principle instead: consciousness should be attributed only when it is useful for explaining and predicting observed behaviors. The authors contend that this principle offers an epistemologically unbiased and operationalizable standard for assessing machine consciousness, avoiding the speculative nature of the computational equivalence view.

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Abstract

Large Language Models (LLMs) have rapidly become a central topic in AI and cognitive science, due to their unprecedented performance in a vast array of tasks. Indeed, some even see 'sparks of artificial general intelligence' in their apparently boundless faculty for conversation and reasoning, Their sophisticated emergent faculties, which were not initially anticipated by their designers, has ignited an urgent debate about whether and under which circumstances we should attribute consciousness to artificial entities in general and LLMs in particular. The current consensus, rooted in computational functionalism, proposes that consciousness should be ascribed based on a principle of computational equivalence. The objective of this opinion piece is to criticize this current approach and argue in favor of an alternative “behavioral inference principle”, whereby consciousness is attributed if it is useful to explain (and predict) a given set of behavioral observations. We believe that a behavioral inference principle will provide an epistemologically unbiased and operationalizable criteria to assess machine consciousness.