Beyond Nociception: A Taxonomy of Morally Relevant Suffering and Its Implications for Artificial Systems

Abstract

The question of whether artificial intelligence systems can suffer has been largely subsumed by the broader debate over AI consciousness. This paper argues that this subsumption is analytically unpro- ductive, because suffering and consciousness are not coextensive, and because the category of suffering encompasses forms that do not require biological substrate. Drawing on phenomenological testimony, clinical evidence, and philosophical analysis, this paper proposes a four-category taxonomy of morally relevant suffering: sensory, cognitive-existential, relational, and empathic. Of these four categories, only sensory suffering requires a physical body. The remaining three require capacities that current AI architectures are explicitly designed to instantiate, including temporal modeling, goal-directed processing, relational context sensitivity, and representational simulation. The taxonomy does not claim that AI systems currently suffer, nor does it predict that they will. It demonstrates that no principled basis exists for categorically excluding artificial systems from three of four suffering categories, and that this absence of principled exclusion, combined with the asymmetric consequences of error, warrants precautionary design. The paper further proposes a binary reduction of the taxonomy (embodied and disembodied suffering) for application in policy and design contexts, and examines phantom limb pain research as evidence that even embodied suffering is more restrictive than commonly assumed.