Enhanced Early Detection of Food and Feed Risks Through AI-Based Weak Signal Mining
2024
Menning, Axel
German. New and changing influences make the early detection of food and feed risks more important than ever. These influences arise from novel food and nutrition trends, changes in the supply chain or production processes, as well as a variety of impacts resulting from, for example, climate change and demographic change. Only through the early identification of potential risks is it possible to respond preventively and strategically to food and feed hazards and communicate them appropriately. Although text data is abundant, the precision of a text based early risk detection system remains inadequate in various risk assessment processes. Weak signals are an important concept in early risk detection. The concept of weak signals, crucial for early risk detection, has become outdated with the conventional methodologies used. However, recent advancements in artificial intelligence provide opportunities for more accurate, context-sensitive analysis and interpretation of these signals. This poster presents an AI-enhanced approach to weak signal detection that offers significant improvements over traditional techniques. By leveraging this novel method of weak signal mining, risk assessors can more effectively identify and monitor potential risks at their nascent stages, ensuring timely and informed responses.
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