Artificial Intelligence & Predictive Analytics for Food Risk Prevention: A discussion paper

Access to food safety insights enables businesses and professionals alike, to identify, monitor and prevent any increasing risks or incidents that need global attention across the complete supply chain. Technology alone is not enough. In order to truly enrich data to create actionable intelligence – for example reactive and predictive insights on the global supply chain – we must integrate human skills and expertise as well. Using scientific or hands-on human expertise can help interpret (and even sense-check) the results thrown out by sophisticated AI algorithms.

Frequently, for example, big data analytics and machine learning are very effective at identifying that something has happened but are less effective at explaining why. That’s where you need the human approach to enrich data insights and create tailormade intelligence to achieve greater visibility across the global food supply chain.

As part of our two EU-funded data related initiatives: "Big Data Grapes" as well as “Cybele” we are investigating ways in which big data and AI can support decision scenarios of high risk and uncertainty. We wanted to understand better which changes we should expect in the near future regarding AI and Predictive Analytics for Food Risk Prevention. We therefore  reached out to the community, asking distinguished colleagues from a variety of supply chain stakeholders about their opinion on what impact predictive analytics are having on the food supply chain and what are the critical food risk assessment and prevention questions that we would expect AI to help answer.

We would like to thank all the contributors for their valuable opinion pieces.

By this volume, we think that we only offer a glimpse at the wealth and variety of challenges ahead. It, therefore, intends to spark and continue the ongoing conversation on the topic. Hopefully giving us better insights into a future that is already here. This is why with pride and pleasure we announce the official publication of this discussion paper:

Agroknow-BigDataGrapes-Discussion_Paper.pdf