Structural Ethical Infeasibility in AI-Enabled Infrastructure Systems: A Constraint-Based Diagnostic Framework

AI-enabled infrastructure systems increasingly govern access to emergency services, dis-aster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently binding, ethical infeasibility is structural rather than algorithmic. We introduce a constraint-based formulation that embeds ethical requirements into the feasible region, and a hierarchical Irreducible Infea-sible Subsystem (IIS) procedure that attributes infeasibility to rule design, algorithmic choice, or physical infrastructure. We further establish the Structural Infeasibility Theorem, deriving closed-form bounds on inter-group disparity across all feasible policies in zone-decomposable problems. Applied to a metropolitan ambulance-dispatch instance, the framework certifies minimum-service infeasibility as infrastructural, shows the efficiency–equity trade-off to be an artifact of constrained infrastructure, identifies pre-investment equity gains as harm redistribution rather than harm reduction, and converts the IIS certificate into a quantified capital-investment specification.