The Architecture of Acceptable Consequences: A Constraint-Based Proposed Solution to the AI Alignment Problem

Most contemporary approaches to AI alignment rely on reward maximization and utility-based optimization. While effective in constrained environments, these paradigms remain vulnerable to reward hacking, goal misgeneralization, and catastrophic instrumental behavior. This paper proposes a fundamental shift in alignment theory: \emph{alignment by acceptable failure}. We argue that moral agency—human or artificial—is not defined by the rewards an agent seeks, but by the worst-case consequences it is willing to accept. A choice is meaningful only if its failure mode is survivable or morally tolerable. Building on this principle, we introduce an AI architecture governed by an \emph{Immutable Moral Kernel}, in which safety is enforced as a non-negotiable boundary rather than an optimization target. By defining a strict safety floor instead of an aspirational moral ceiling, this framework ensures that artificial intelligence remains permanently bounded within human-tolerable failure modes.