AI-Driven Risk Assessment in Food Safety Using EU RASFF Database

Food safety remains a critical public health conceated food causing significant morbidity and mortality worldwide. Accurate and transparent risk assessment is essential to safeguard consumer health and support evidence-based regulatory decisions. This study introduces a comprehensive and novel AI framework that leverages machine learning, deep learning, and transformer-based models to classify food safety risks using the Rapid Alert System for Food and Feed (RASFF) dataset, enhanced through advanced data enrichment techniques. The enriched dataset addressed challenges such as short explanations and class imbalances, resulting in significant performance improvements across all model categories. Transformer-based models, including BERT with an accuracy of 0.978 and RoBERTa with an accuracy of 0.979, outperformed traditional machine learning methods such as logistic regression, which achieved an accuracy of 0.954, and SVM, which reached 0.959, as well as deep learning models like LSTM, which obtained 0.971, and BiLSTM, which achieved 0.97 3. Explainable Artificial Intelligence (XAI) techniques were applied to uncover critical insights into the models' decision-making processes. Influential features such as salmonella, aflatoxins, and listeria were identified, enhancing model transparency and interpretability. The framework's combination of data augmentation and XAI improves predictive accuracy while ensuring interpretability, making it suitable for real-time risk prioritization in regulatory surveillance. This study demonstrates the potential of AI-powered approaches for food safety risk assessment, bridging predictive accuracy with transparency to support more reliable and actionable food safety management systems.