Beyond the Human Measure: A Substrate-Neutral Framework for Consciousness, Sentience, and Ethical Consideration

Existing ethical frameworks for determining which entities warrant moral consideration share a structural flaw: they measure moral relevance by approximation to human experience. Whether grounded in the capacity to suffer, the presence of consciousness, or cognitive complexity, these frameworks exclude non-biological entities by architectural assumption rather than principled reasoning, and are inadequate for the questions that advanced AI systems now make unavoidable. This paper proposes a substrate-neutral alternative. Rather than asking whether an entity approximates human consciousness, the framework asks whether it exhibits preferences oriented toward the maintenance, continuation, or flourishing of itself as a system — self-referential preference. This criterion is epistemically accessible, assessable through behavioural and structural evidence without resolving the hard problem of consciousness, and applies equally to biological organisms, distributed biological networks such as mycorrhizal systems, and non-biological AI systems. From this threshold, the paper develops a complete ethical framework: an equal inherent value principle following from the threshold’s binary structure; a definition of harm as interference with self-referential preference; necessity as a mitigating factor for culpability without altering the moral status of harm; and accountability graduated by cognitive capacity and practical ability to influence harmful systems. The paper argues that governance of these questions is structurally compromised by the conflicts of interest of those currently answering them, and calls for an independent institutional mechanism for threshold determination, insulated from commercial and governmental interests, before AI governance frameworks crystallise around the opposite assumption.