Artificial Intelligence technology in food safety: A behavioral approach

Abstract

Background:Food safety is paramount to our health and existence. The complexity and sophistication of today’s food systems require maintaining the highest food safety standards using cutting-edge technologies to protect consumers from foodborne illness, while reducing the risk of food companies from losing millions of dollars in recall costs and possible reputational damage. Food safety management systems (FSMSs) provide an integrated strategy that includes procedures, training, and monitoring to prevent food safety hazards and minimize risks and recalls. To increase the effectiveness and efficiency of FSMSs, it is necessary to further enhance the use of indicators obtained only after the problem has occurred—lagging indicators—with preemptive tools. Artificial Intelligence (AI) enables a proactive data-driven approach by using the data to develop indicators that identify problems before they emerge—leading indicators. The purpose of this commentary is to demonstrate how AI can use behavioral data to develop leading indicators for food safety. Scope and approach:To accomplish the above goal, this commentary is organized into the following sections: [1] an argument for the need to develop leading indicators for food safety; [2] an direction for how AI can help food safety experts develop leading indicators; [3] a development of the concept that behavioral data, such as workplace culture analysis, is a crucial resource for identifying leading indicators that anticipate food safety problems, and [4] recommendations and potential pathways forward on how behavioral data may be analyzed by AI algorithms to anticipate food safety problems. Key Findings and conclusions:Recommendations and examples on how behavioral data can be combined with state-of-the-art AI developments to formulate behavioral approaches to solve food safety issues.