How do we know if artificial intelligences are unfeeling algorithms or conscious beings that experience sensations and emotions? The answer to this has major implications on what ethical guidelines we apply to AIs. If we believe that a future AI experiences pain and joy, we’d treat it differently than we’d treat a simple algorithm like an Excel formula. Can we know what an AI is experiencing? Theories of consciousness aim to say what leads to consciousness and could help determine if AIs, animals, or even trees are conscious.
One of the foremost theories, Integrated Information Theory (IIT), is unique in that it comes with an equation for calculating how conscious any given thing is. A human brain scores very high while a pile of rocks scores zero. Electronic circuits from your kitchen lights to ChatGPT can have a wide range of scores. Regarding the latter and other artificial intelligences, IIT makes an interesting prediction: AIs built from complex, looping architectures will have at least some consciousness while those from linear, feedforward networks (ChatGPT included) will have zero consciousness.
If this theory can be proven true, it will be immensely useful for the future of AI ethics, not to mention for understanding human consciousness. Already, the theory is being used to predict if a patient in a vegetative state is truly unconscious or merely locked-in, unable to move yet perceptive of their surroundings. However, the provability of Integrated Information Theory has come into question recently, with a 2019 paper titled The Unfolding Argument. The argument doesn’t say that IIT must be false, but rather that it can never be proven true. It hinges on the fact that IIT predicts different levels of consciousness for networks that are shaped differently but behave identically.
To fully understand the argument and what it means for our understanding of consciousness, let’s dive into consciousness, IIT, recurrent versus feedforward networks, and the unfolding argument.