VietCheckMed: Explainable Regulatory Compliance Checking for Medical Advertisements on Vietnamese Social Media - Tam, Nguyen Thanh; Tran, Khanh Quoc; Pham, Dat Thanh; Phu Le, Truong; Han, Nguyen Hoang Gia; Nguyen, Binh T. (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Regulatory compliance checking for online medical advertisements poses a critical public safety challenge distinct from traditional fact-checking, particularly in low-resource languages. Existing automated systems are ill-suited for the authorization-based, evidence-grounded, and explainable reasoning this task demands. To address this gap, we introduce VietCheckMed, a novel retrieval-augmented framework, and VietAestheticAds, the first large-scale, expert-validated benchmark for this task, comprising 8,329 advertisements paired with an authoritative regulatory corpus of 9,978 facilities. Comprehensive experiments demonstrate that our evidence-grounded approach is essential, substantially outperforming powerful unassisted LLM baselines by over 0.3805 F1-score. A detailed analysis reveals that the primary remaining challenges are nuanced failures in semantic and logical reasoning, defining a clear frontier for future research. To promote advances in regulatory technology and responsible AI, our dataset, code, and evaluation scripts will be made publicly available. This work contributes a foundational methodology and a vital public resource for developing responsible AI in high-stakes regulatory domains.