Safeguarded AI-Driven Semantic Communication: Design Principles, Architecture, and Challenges

Semantic communication (SemCom) systems heavily rely on artificial intelligence (AI) models to generate semantic representations and reconstruct original meaning. However, current AI models in SemCom systems present reliability concerns due to two major challenges. First, they often operate on a “best effort” basis, displaying unwarranted overconfidence that undermines fidelity in semantic reconstruction. Second, they are lack of generalization capabilities to adapt to changing network conditions and varying knowledge bases (KBs) in SemCom networks. Communication systems are safety-critical, which cannot tolerate the unreliable transmission caused by unsafe AI behavior. In this paper, we establish a safeguarded AI framework for SemCom networks that constrains AI behavior through defined safety mechanisms and boundaries to address the two challenges mentioned above. Particularly, we present safeguarded AI’s architecture, detailing the designs of world model, safety specifications, and gatekeepers. Notably, gatekeepers enforce safety constraints on AI output based on defined safety specifications. We also implement a real-world safeguarded AI-driven SemCom framework with periodically updating KBs to absorb new environmental data and users’ feedback. Furthermore, we validate our framework’s effectiveness through a case study with simulation results. Finally, we explore additional challenges of the proposed framework to guide future research directions.