Data Readiness on Artificial intelligence

The European Food Safety Authority (EFSA) together with EFSA’s Advisory Group on Data (AGoD) are pleased to announce that a symposium dedicated to exploring the transformative impact of Artificial Intelligence (AI) technologies within the food safety ecosystem took place in Parma in presence on 23 and 24 October. It has been a unique opportunity to:

• Discuss AI Opportunities and Requirements: Gain insights into the opportunities and essential requirements for developing and implementing AI systems in food safety.
• Learn from Experts: Hear from leading experts about their experiences with AI and data management in food safety.
• Share and Collaborate: Exchange project ideas, network with peers, and collaborate on innovative solutions.

The event was by invitation only to members of EFSA’s Advisory Group on Data and its subgroups, EFSA Advisory Forum members, EFSA Scientific Data networks members, EFSA’s Focal Points and experts involved in Focal Point Data Tailor Made Activities. The organising committee ensured geographical balance and a fair representativeness and pertinence to the technical topics of all participants in the audience.

Presentations and symposium report are available here below

Day 1 - Opening session

• Abbinante - AGoD roadmap
• Baggesen - SG: People & Capacity
• Józwiak - AGoD Innovation subgroup
• Józwiak - EFSA Advisory group on data
• Scharfenberg - AGoD SG Data Management
• Patterson - The FDA AI Program: Challenges & Opportunities
• AGod Roadmap combined

Day 1 - Posters

• Benfenati - sOFTERA
• Ben Romdhane - Promoting the Adoption of the FAIR Scientific Knowledge eXchange
• Broman - Data Mapping Sweden
• Lattanzio - FS4EU & HoliFood
• Cerella - Poster SIGMA-EST
• Nabais - Tool for data Capture
• Spill - Ontologies

Day 2 - Opening session

• Margoni - Generative AI: opportunities and challenges from a legal (copyright) perspective
• Reis - AI Opportunities and Challenges in Public Administration and Food Safety

Day 2 - Parallel session 1 - AM - Explainable and Open Science

• Kutterer - AI in Food Safety: Navigating the Transparency-AI Safety Paradox
• van der Velden - Explainable AI for food safety

Day 2 - Parallel session 1 - PM - Data mapping with AI

• Broman, Foster - Data mapping tool
• Studer - Translating raw text into standardised text using AI
• Walsh - Large Language Models (LLM) Analysis For Automatic Foodex2
  Codification

Day 2 - Parallel session 2 - AM - Collaborative learning Data and models sharing

• Löhn - Collaboration to Strengthen AI Expertise in Departmental Research
• O’Sullivan - Food Safety Data: Trusting, Sharing, and Understanding

Day 2 - Parallel session 2 - PM - AI for risk assessment I

• Eftimov - Food Data Information and Relation Extraction, Lessons from the Past and the AI Challenges Shaping Our Future
• Menichetti - Decoding the Foodome Project: Molecular Characterization of Food and Reproducible Health Scores
• Sebek - Activating Food Composition Data Using AI
• van de Schoot - On transparency and reproducibility of AI-aided systematic reviews

Day 2 - Parallel Session 3 - AM - Semantics & Ontologies

• Ben Romdhane - Leveraging Linked Data and Ontologies to enhance Generative AI
• Janosov - Understanding the Foodome via Network Science
• Whaley - GRADE as an example of a methods ontology

Day 2 - Parallel session 3 - PM - AI for risk assessment II

• Leekitcharoenphon - Applying sequencing and machine learning for improving food safety
• Njage - Artificial Intelligence in Tracking and Assessing Food Pathogen Risks and Drivers: Applications and Data Needs
• Schneider - Applied Machine Learning in Food Authenticity using the example of cactus oil

Day 2 - Closing session

• Devalier - Regulatory art, data art (and the missing human factor)