Modern decision-making for complex physical and engineered systems increasingly requires the ability to quantify high-dimensional uncertainties and make decisions by solving risk-averse optimization problems under uncertainty—all in near real-time. Operator learning has emerged as a promising framework for enabling scalable surrogate modeling in this context. However, such approaches inevitably introduce approximation errors that can degrade the quality of downstream decisions.