What is consciousness?
One of the challenges in studying consciousness in AI is defining what it means to be conscious. Peters says that for the purposes of the report, the researchers focused on ‘phenomenal consciousness,’ otherwise known as the subjective experience. This is the experience of being — what it’s like to be a person, an animal, or an AI system (if one of them does turn out to be conscious).
There are many neuroscience-based theories that describe the biological basis of consciousness. But there is no consensus on which is the ‘right’ one. To create their framework, the authors therefore used a range of these theories. The idea is that if an AI system functions in a way that matches aspects of many of these theories, then there is a greater likelihood that it is conscious.
They argue that this is a better approach for assessing consciousness than simply putting a system through a behavioral test — say, asking ChatGPT whether it is conscious, or challenging it and seeing how it responds. That’s because AI systems have become remarkably good at mimicking humans.
A theory-heavy approach
To develop their criteria, the authors assumed that consciousness relates to how systems process information, irrespective of what they are made of — be it neurons, computer chips, or something else. This approach is called computational functionalism. They also assumed that neuroscience-based theories of consciousness, which are studied through brain scans and other techniques in humans and animals, can be applied to AI.
On the basis of these assumptions, the team selected six of these theories and extracted from them a list of consciousness indicators. One of them — the global workspace theory — asserts, for example, that humans and other animals use many specialized systems, also called modules, to perform cognitive tasks such as seeing and hearing. These modules work independently but in parallel and share information by integrating into a single system. A person would evaluate whether a particular AI system displays an indicator derived from this theory, Long says, “by looking at the architecture of the system and how the information flows through it.”
Seth is impressed with the transparency of the team’s proposal. “It’s very thoughtful, it’s not bombastic, and it makes its assumptions really clear,” he says. “I disagree with some of the assumptions, but that’s totally fine because I might well be wrong.”
The authors say that the paper is far from a final take on how to assess AI systems for consciousness, and that they want other researchers to help refine their methodology. But it’s already possible to apply the criteria to existing AI systems. The report evaluates, for example, large language models such as ChatGPT and finds that this type of system arguably has some of the indicators of consciousness associated with global workspace theory. Ultimately, however, the work does not suggest that any existing AI system is a strong candidate for consciousness — at least not yet.