ResilientAI: An Integrated Framework for Detecting and Addressing Domain Shifts in Dynamic Data to Enhance the Robustness and Reliability of Machine Learning Models for Food Safety

ResilientAI aims to develop an intelligent framework for enhancing the robustness, reliability, and interpretability of machine learning models used for food safety risk prediction. The project addresses a fundamental challenge in the deployment of AI systems: the deterioration of predictive performance when models encounter evolving data distributions, known as domain shifts. By combining multimodal learning, continuous data monitoring, explainable AI, and adaptive machine learning, ResilientAI seeks to support reliable risk assessment and informed decision-making in the food supply chain.

The project focuses on the development of advanced methodologies for integrating heterogeneous data sources, including historical food safety incidents, environmental conditions, agricultural practices, and supply chain information, to improve the prediction of emerging food safety risks. Particular emphasis is placed on the early detection and characterization of domain shifts, uncertainty quantification, and the generation of interpretable predictions. Novel domain adaptation and generalization strategies will be investigated to enable predictive models to maintain their performance under changing conditions, while providing insights into the factors influencing their predictions and potential corrective actions.

The developed methodologies will be integrated into FOODAKAI, AGROKNOW's commercially available AI-powered food safety intelligence platform, and evaluated through representative use cases involving the prediction of food contamination risks, particularly those associated with mycotoxins and heavy metals. The project will also develop interactive tools that enable food safety professionals and data analysts to monitor model performance, understand detected distribution shifts, and support appropriate adaptation strategies.

Through the collaboration between ICCS–NTUA and AGROKNOW, ResilientAI aims to bridge advanced AI research and industrial applications, contributing to more transparent, adaptive, and trustworthy decision-support systems for food safety. The project is expected to enhance the capacity to anticipate and mitigate emerging risks, strengthen food quality assurance practices, and ultimately contribute to the protection of public health.