Trust Deficits Hinder AI Food Safety Data Sharing

A recent study published in npj Science of Food identifies trust, rather than technological capability, as the primary barrier to leveraging artificial intelligence for food safety. Research led by Cornell University doctoral candidate Linda Kalunga, under the supervision of Professor Renata Ivanek, analyzed interviews with twenty-seven senior food industry professionals spanning dairy, meat, produce, manufacturing, and laboratory sectors. Collaborators from the University of California, Davis, and the University of California, Berkeley, also contributed to the interdisciplinary project. The findings reveal a widespread industry consensus on the potential of AI to transform food safety. By aggregating large, diverse datasets, companies can identify emerging risks earlier, enhance predictive modeling, and detect rare foodborne outbreaks that individual organizations might otherwise miss. Smaller enterprises, in particular, stand to gain from access to analytics that would otherwise demand prohibitive research investments. Despite these clear advantages, the study found that collaborative data sharing remains largely unrealized due to systemic trust deficits and operational fragmentation. Participants consistently cited technical incompatibilities, including uneven digital infrastructure and reliance on legacy recordkeeping systems, as logistical hurdles. However, the dominant concern proved psychological and legal. Executives repeatedly expressed anxiety over ceding control of proprietary information, fearing data misuse, regulatory scrutiny, antitrust implications, or competitive disadvantages if information were shared with rivals. One respondent noted that unreleased data could effectively be weaponized against their organization, underscoring a fundamental paradox: while industry-wide benefits are distributed collectively, data-sharing risks are borne individually. To bridge this divide, the researchers recommend establishing standardized data protocols and deploying neutral third-party intermediaries, such as academic institutions or independent consortia, to manage privacy protections and governance. The study emphasizes that realizing AI’s full potential in food safety requires shifting focus from algorithmic development to ecosystem coordination. Solving these systemic challenges demands sustained interdisciplinary collaboration across food science, data analytics, economics, and public policy. Ultimately, the research positions institutional trust and clear regulatory frameworks as indispensable prerequisites for next-generation food safety infrastructure.