Artificial Intelligence and Electrical & Electronics Engineering: AIEEE Open Access
A Layered Terminal-Goal Framework for AI Alignment: Quantilized Optimization, Irrevocable Constraints, and a Conformance Regime
Abstract
Ralph C. Ennis
The Alignment of advanced artificial intelligence systems with human values remains a central challenge in safety research. This paper specifies a terminal-goal framework that defines and enforces a bounded terminal objective: the sustained flourishing of four protected domains (physical, epistemic, cultural, and relational) across humanity and artificial intelligence. The framework separates the terminal goal, irrevocable constraints, and enforcement into distinct layers of a six-layer governance stack whose lowest layer gates every agent action before execution. Quantilization replaces unbounded maximization, and the specification is developed analytically from the quantilizer cost bound, lexicographic constraint ordering, and one-sided binomial confidence bounds. The resulting specification comprises a quantilized terminal goal whose quantile parameter is clamped in code between 0.01 and 0.25; five lexicographically ordered constraints with an orthogonal fail-safe reversion procedure; shadow-metric measurement; a multi-tradition epistemic- humility ensemble; a four-stage deployment pathway with explicit gate criteria; and an eight-requirement conformance regime. The analysis identifies four boundaries at which specification and implementation diverge by construction: scorer circularity, substrate-dependent enforcement, the claim ceiling, and the advisory character of behavioral-rule layers. Under the claim ceiling, no deployment may assert a net safety improvement until its marginal catch rate and false-assurance rate have been measured against the governed model and reported together. The framework is implementation-oriented, reports no empirical results, and makes no claim to solve alignment for superintelligent systems.