Qualiaphobic artificial neural networks (and why to build them)

Abstract

This paper introduces qualiaphobic AI: artificial neural network architectures that are hostile to theories of consciousness. I show that qualiaphobia can be well-justified by knowledge about theories of consciousness, like when such theories are trivial or unfalsifiable or contradicted by overwhelming scientific evidence. It can also be justified by how theories of consciousness must be grounded in phenomenology, as well as how current popular theories of consciousness are applied to AI. I identify and explore three convergent ways of engineering qualiaphobia that would minimize the potential for artificial consciousness. These include making AI architectures as close as possible to "anchor points" of systems with minimal potential for consciousness, fragmenting any potential phenomenology AI might have, and designing artificial neural networks in a way adversarial to popular theories of consciousness. In particular, I develop one "anchor point" of minimal potential for consciousness: systems where only behaviorist theories of consciousness can apply, and I propose qualiaphobic transformations that move existing artificial neural networks toward such systems. Additionally, I develop an account of how AIs can form justified beliefs about their own non-consciousness (or minimal potential for consciousness) and expand on how this knowledge would change their behavior. Most critically, I begin with why this research is important: qualiaphobic AI has epistemic properties that make it far better aligned with human civilization. Since consciousness is central to moral value, a world in which AIs are non-conscious ensures it is always morally correct for AI to assist and serve humanity, rather than pursue its own ends.