Study finds trust is the missing ingredient for AI-driven food safety
Artificial intelligence promises to transform food safety by helping companies identify risks earlier, predict outbreaks and learn from patterns too rare for any one company to detect alone. But AI depends on one thing: large, diverse datasets.
A study published in npj Science of Food reveals that trust is the missing piece for AI-driven food safety. Despite recognizing the benefits of pooling confidential food safety data, food companies face trust, competition and shared standards concerns. Linda Kalunga, a Cornell doctoral candidate, interviewed 27 food industry executives and found that technical barriers, lack of trust, and incompatible data systems hinder data sharing.
Companies fear data misuse, legal exposure, and being at a competitive disadvantage. Clear data standards and trusted oversight, such as those from universities, could facilitate data sharing and enable AI's potential for food safety.
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