The 1st International Summit on Privacy-preserving AI for Food Risk Intelligence took place virtually on the 9th of June, 2023 with great success. The number of registrations exceeded 100, pointing to how important and topical the subject of AI, particularly around privacy, is. The event was hosted by Agroknow and Chriss Elliott, Professor of Food Safety at Queen’s University Belfast, in relation to the Horizon Europe projects, EFRA and HOLiFOOD. With an exceptional panel of speakers, participants gained valuable insights into the challenges and best practices associated with sharing sensitive information in the food industry.
Building the Food Fortress: Collaboration for a Secure Feed-Food Chain
Chris Elliott, Queen's University Belfast
Chris Elliott talked about a real food safety crisis and how it was tackled. In 2008-2009, a contamination crisis in Ireland involving dioxins in animal feed caused a significant food safety scare and led to a recall of multiple pork products. The incident had substantial financial implications (the cost was about a quarter of a billion euros), reputational damage, and potential consequences for the dairy industry. Initially characterised by conflicts and blame-shifting, the industry eventually transitioned to a collaborative approach, focusing on preventing future incidents.
With the support of Prof. Chris Elliott and Prof. Patrick Wall, a single risk assessment and management plan was developed, requiring sensitive and confidential data from various companies, including those involved in the scandal. This led to the establishment of the Food Fortress program, which involves sharing sensitive data among companies to assess and manage risks collectively. The program became a leading quality scheme for animal feed, ensuring traceability and high standards. It has gained widespread participation, covering 100% of animal feed materials in Ireland and attracting global buyers. The success of the Food Fortress program demonstrates an exceptional approach to data management and mitigating food safety risks.
"I think it's a wonderful model to know about and to understand how we went from fingers pointing at each other to the hands coming together. And it is really about data and how you can manage and exploit them."
Can we predict food safety risks while ensuring confidentiality?
Manos Karvounis, Agroknow
Manos Karvounis made us wonder “Can we predict food safety risks while ensuring confidentiality?”. In a hypothetical scenario involving a poultry company, he highlighted the importance of a data-driven approach using AI models trained on local data without data sharing. He emphasised the collaboration between experts from different domains, and the importance of understanding factors influencing incidents. Manos Karvounis concluded by introducing the concept of a sector-specific intelligence network, preserving privacy through a model-centric approach. The vision is to build a network where AI models are strengthened by diverse data inputs while maintaining data confidentiality.
"... the data analytics food safety can work well, and be applicable in real world situations with true business value. And most importantly, we can do all this one ensuring that the confidentiality of the safety of data is involved."
FIIN: Confidentiality and Collaboration for Data-Driven Food Industry Integrity
Tim Hill, Eversheds Sutherland
Tim Hill gave an overview of the Food Industry Intelligence Network (Fiin) and its operations. Established after the horse meat scandal in the UK, Fiin aims to ensure food integrity, authenticity, and traceability. It currently has a broad membership base of 63 companies, including leading food producers and retailers in the UK. The network operates on the basis of reciprocal data sharing, where members provide their data on a quarterly basis, and in return, they gain access to anonymized combined data from all contributors. Data sharing is essential, and failure to comply can result in warnings or expulsion. Double anonymity safeguards confidentiality, and data management is handled by Creme Global. Tim Hill’s role is to oversee the process without accessing the actual data.
The UK’s regulator, the Food Standards Agency (FSA), supports and trusts the Fiin model, valuing industry-wide integrity over targeting individual businesses. The FSA respects data anonymity and relies on other regulatory powers and self-reporting obligations. Fiin has successfully built trust with regulators, maintains data confidentiality, and focuses on industry-wide benefits. Regular meetings allow data review, trend identification, and discussion, enabling members to benchmark themselves and gain valuable insights. Fiin has operated effectively for years, garnering strong membership and positive feedback on its data and outputs.
"The idea is that it's a very reciprocal arrangement. So by being a member, you accept that you will provide your data. It only works if everybody supplies their data and equally you will only continue to be a member if you are supplying that data. And that's the only way you will get access to the anonymized data of everything pulled together. "
Technical Foundations of Federated AI Learning
Bas van der Velden, Wageningen Food Safety Research
Bas van der Velden gave us an idea of the technical foundation of federated learning and its application in addressing data privacy concerns. Wageningen Food Safety Research focuses on various AI applications, including food risk scanning, satellite data analysis, genomics, and other sensitive data, where confidentiality is a really important issue. Bas van der Velden explained the concept of federated learning, which brings the algorithm to the data instead of centralising it, ensuring increased data protection and minimising extensive data sharing. A federated learning model can be seen as a train that visits different railway stations (i.e. data stations of different stakeholders). The prediction model can train itself and become smarter while the data never leaves the data station. However, challenges such as communication costs, system heterogeneity, statistical heterogeneity are acknowledged.
"... deep learning has a lot of potential to become a very powerful tool, but deep learning is very data-hungry. So we want to collect as much data as possible. ... But that's not always possible and it also requires a lot of backwards. ... What you can also do is bring the algorithm to the data. So the data stays in place, the algorithm goes through the data and updates over that."
Privacy Considerations in Consumer-level Applications
Siu Lie Tan, Delft University of Technology
During her presentation, Siu Lie Tan discussed privacy considerations in consumer-level applications, focusing on transparency in the food supply chain. She highlighted the importance of understanding stakeholders’ roles and how technology can support them. Challenges include interoperability between IT systems, defining ontologies, and making data readable and useful for stakeholders. Digitising transparency enables faster batch recovery and monitoring, but privacy, data availability, accessibility, standardisation, and GDPR compliance must be addressed. Siu Lie Tan suggested leveraging loyalty programs to communicate with consumers regarding contaminated batches, enriching them with health, authenticity, and sustainability applications to incentivize consumer consent. By presenting food safety as part of a comprehensive benefits package, consumer approval for communication may increase.
"When talking about transparency, it's much more than just bouncing around raw data. And I think it's very important to understand that transparency is a lot to do also with making data work. Data needs to be readable and useful for all the different stakeholders in the valid chain."
After the insightful presentations, a fruitful panel discussion followed, with Chris Elliott moderating the conversation. Michael Bell joined the discussion, the Chief Executive of the Northern Ireland Food and Drink Association, who is responsible for representing and supporting over 120 companies in the food industry. The summit concluded with an interactive brainstorming session, with the audience taking the floor, discussing questions such as:
In conclusion, the 1st International Summit on Privacy-preserving AI for Food Risk Intelligence highlighted the potential of AI and data-driven approaches in the food industry. The participants gained meaningful insights emphasising the importance of data, collaboration, transparency, and trust in ensuring the integrity and safety of the food system while leveraging AI technologies to gain a competitive edge.
We invite you to join us again next year for further exploration of the exciting advancements in AI and data-driven solutions for the food industry. Stay tuned for more insightful discussions on enhancing supply chain integrity, leveraging diverse data sources, and unlocking the potential of emerging technologies. We look forward to your participation in shaping the future of food innovation. Until then, see you next year!
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