A survey on explainable AI for semantic communication: Architecture, challenges, and future opportunities

As communication systems evolve toward 6G, semantic communication is emerging as a transformative paradigm that prioritizes the accurate transmission of meaning rather than just bits. While artificial intelligence enables this shift by facilitating intelligent interpretation and context-aware processing, it also introduces significant challenges related to transparency, reliability, and user trust. XAI has thus become essential in making AI-enabled semantic communication systems more interpretable, auditable, and accountable. To the best of our knowledge, this is the first survey that systematically analyzes how explainability can be embedded across all stages of the semantic communication pipeline, integrating architectural design, metrics, security considerations, and human-in-the-loop mechanisms. Additionally, the survey identifies pressing research challenges, including the lack of standardization, real-time applicability, and vulnerabilities introduced by opaque AI models. By drawing attention to these issues and outlining future research directions, this survey aims to guide the development of responsible and trustworthy semantic communication systems for next-generation wireless networks.