From Ethical Principles to Enforceable AI Systems: A Systems Engineering Perspective

Abstract

Ethical principles for artificial intelligence are widely articulated across research, policy, and industry discourse. How ever, the translation of these principles into enforceable system behavior remains an unresolved challenge. This work examines the gap between ethical intent and operational reality from a systems engineering perspective. It argues that ethical AI cannot be achieved through model level constraints alone and must instead be embedded within the architecture, lifecycle management, and governance mechanisms of AI systems. A structured engineering methodology is proposed that integrates ethical requirements into data pipelines, learning workflows, validation processes, and deployment controls. Empirical evaluation across representative workloads demonstrates that enforceable ethical controls can be operationalized without prohibitive performance tradeoffs. The results indicate that system level design choices are decisive in transforming ethical aspirations into measurable and auditable AI behavior.