Recommendations to the FSA

1. Publish Guidance on Responsible Use of AI to Assure Food Safety and Regulatory Compliance 

The FSA should provide guidance for Food Business Operators (FBOs) making clear that AI must act only as a decision-support tool, with humans retaining accountability. AI outputs should remain transparent and explainable to underpin regulatory compliance and ensure safety-critical decisions are not delegated to AI agents of dependent systems. Food businesses should be aware of limitations and work with suppliers to ensure tools are fit for purpose. 

2. Establish Ongoing Monitoring of AI Systems and Potential Impacts 

The FSA’s Strategic Insight Team should regularly monitor AI adoption by Food Business Operators and its impacts across the food system, providing early warning of risks and ensuring regulatory responses remain proactive. 

3. Promote Data Assurance, Validation, and Standards Alignment 

The FSA should continue its promotion of data quality, provenance and standards (including data and cybersecurity) to help realise fair, auditable AI and, including the promotion of measures to ensure SMEs and smaller FBOs are not disadvantaged. 

4. Support the Development of Standards and an Industry-Led Code of Practice for Assuring AI in Food Safety 

The FSA should promote the development of test and validation standards for AI systems and a Code of Practice to food businesses, technology suppliers, standards bodies, potentially acting as a convenor to ensure alignment with regulatory expectations and wider assurance frameworks. 

5. Engage with Broader Regulatory and Policy Perspectives

 The FSA should collaborate with other UK and international regulatory authorities and government agencies to align governance, ethics, and auditability standards, drawing lessons from industries with advanced AI regulation. 

6. Commission Research on Human Behaviour and Interaction with AI in Food Safety Contexts 

The FSA should advocate for behavioural research that studies how FBO workers and directors engage with AI, addressing issues such as overreliance, trust, and cognitive bias to inform better training, guidance and governance.