An Agnostic Ethical Floor for AI Governance

Whether a particular AI system harms the people it affects currently depends on which organization built it and what that organization considers acceptable. This paper argues that the question has a structural answer independent of corporate policy. Drawing on a formal model of experiential value built from first principles, the paper derives a taxonomy of five structurally distinct harms: region collapse, interference distortion, state-space deformation, resolution failure, and termination. Each corresponds to a specific component of the model, differs in mechanism and reversibility, and requires a distinct governance or engineering intervention. The taxonomy was tested against common classes of AI system design and demonstrates that systems currently treated as a single regulatory category inflict structurally different harms requiring different instruments to address. The model also accounts for what happens when conscious systems interact, generating testable predictions about phenomena from radicalization to grief to caregiving that existing frameworks treat as unrelated. From that analysis, the paper derives an agnostic ethical floor applicable to any AI system interacting with conscious subjects: the observer’s capacity for self-referential state determination must be preserved.