Food industry leaders see potential in pooling confidential safety records to spot risks, but worries about trust, competition and inconsistent data systems are holding back that collaboration, Cornell researchers found.
The peer-reviewed study in npj Science of Food drew on in-depth interviews with 27 U.S. food-industry leaders from dairy, meat, produce, manufacturing and laboratory services. A Cornell summary said participants saw a possible benefit in larger shared datasets for detecting rare problems and improving predictive tools, but the research did not test an AI system or show that data sharing has reduced outbreaks.
Participants described incompatible recordkeeping, uneven digital infrastructure and small companies' continued use of spreadsheets or paper files. They also worried that sensitive data could be misread, invite regulatory or legal consequences, or give competitors an advantage.
Lead researcher Linda Kalunga said the potential gains from shared analysis would be spread across the industry while the risks of releasing data fall on individual companies. Interviewees pointed to clearer standards, privacy protections and neutral third parties as possible ways to build confidence.
The interviews were conducted from June 2023 to January 2024. Researchers recruited participants purposively and through referrals, then analyzed their responses for themes. That qualitative design identifies concerns among the people interviewed; it does not measure how common those concerns are among all food companies or prove that trust is the single largest barrier nationwide.
The authors said data governance would need to address control, access and protection before wider pooling could support AI-based food-safety work. The study does not provide New York-specific results.
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