Aaron Smith

This project tackles four bottlenecks in digital food safety across the farm-to-table supply chain by developing algorithms, data standards, and frameworks for privacy and text analytics. Focus areas include: 1) Active learning for monitoring optimization, 2) Standardized data for sharing, 3) Privacy-guaranteed data sharing, and 4) Machine learning for text analysis.

Our work identifies key stakeholders and critics of AI technologies, learns their perspectives on core AI and food system issues, and examines points of resistance (e.g., hesitation, legal, logistical, or ethical barriers) and relational tensions. We aim to reframe problems from their viewpoints to ease tensions, foster dialogue, and build pathways forward.

Researchers investigates the potential of data-driven innovation in achieving digital "smart" food safety by interviewing industry leaders about sharing safety data. The team will then create curated datasets and develop digital twins for food systems which will integrate into a comprehensive retail food safety and spoilage model for dynamic risk assessment.