This paper develops an observable-only, no-meta theory of universal liberation and welfare viability for human and artificial subjects. Under the assumptions that no unchallengeable meta-evaluator is available and that certificate-relevant premises must be observable or explicitly marked as non-authorizing conjectures, the theory defines what can be scientifically certified: not hidden happiness itself, but scoped lower-bound viability of observable welfare-support and liberation conditions. The framework combines measurable histories, filtrations, ordered value spaces, noncompensable floors, viability domains, imprecise probability, causal identification sets, controlled transition laws, stratified validators, protected-group envelopes, population-solvency constraints, and backdated refutation rules. It treats artificial-unit creation, copying, simulation, and deployment as duty-generating processes governed by instantiation permits, tail-risk solvency, and copy-control requirements. The resulting theory is designed as a pure but implementation-oriented certification framework for welfare support, AI welfare uncertainty, human-AI governance, and open-world duty allocation under partial observability. The main results show limits on hidden-happiness maximization and unscoped universal happiness claims, justify order-valued noncompensatory welfare representation, establish conservative uncertainty and debt propagation, provide measurable executable selection conditions, prove robust one-step viability under controlled lower probabilities, block cross-subject compensation through protected meets, prevent open-world caution from collapsing into paralysis, and require refutations to be replayed from conservative occurrence intervals.
K Takahashi (2026) studied this question.