Beyond computational equivalence: the behavioral inference principle for machine consciousness.

The current consensus for attributing consciousness to artificial entities like Large Language Models relies on computational functionalism, which proposes that consciousness should be ascribed based on computational equivalence. This opinion piece criticizes that approach and argues for an alternative 'behavioral inference principle', whereby consciousness is attributed only when doing so helps explain and predict observed behaviors. The authors believe this principle offers an epistemologically valid and operationalizable criterion for assessing machine consciousness.

Study at a glance

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, have 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 valid and operationalizable criterion to assess machine consciousness.