AI and Food Safety: Can We Trust Machine-Generated Food Advice?

AI vs. Food Safety

The year is 2026, and a home cook in Auckland asks a voice assistant whether leftover rice left at room temperature for eight hours is safe to reheat and eat. The assistant confidently says yes. In Singapore, a food safety manager queries a generative AI chatbot for the regulatory allergen labelling thresholds for sesame in packaged foods, and the model returns a figure that is almost correct, off by a decimal point, and referencing a regulatory version that was superseded two years ago. In both cases, the AI responded fluently, confidently, and incorrectly. Nobody died, but the pattern is concerning, and in food safety, patterns like this eventually produce outcomes that matter very much. Thus, artificial intelligence is reshaping food safety communication, but also introducing new and poorly understood risks.

 

This is not a theoretical argument against artificial intelligence. AI is already delivering real and verifiable benefits across the food safety domain — from pathogen detection in laboratory settings to predictive import surveillance at border controls. The Food and Agriculture Organization of the United Nations (FAO), in its landmark 2025 technical publication developed jointly with Wageningen Food Safety Research, reviewed 141 scientific papers and documented practical AI deployments across inspection, surveillance, border control prioritisation, regulatory efficiency, and risk communication[1]. The report positions AI as a present-day tool, not a future aspiration. That is important context. AI in food safety is not hype — it is happening, and in many cases, it is working.

 

The Communication Gap

Traditional food safety communication had a relatively clear architecture. Regulatory bodies published guidelines. Industry implemented them. Accredited laboratories verified. Certified professionals interpreted the results. Consumers received simplified messages through labelling, public health campaigns, and their general practitioners. The chain was imperfect, but accountability was traceable, which is the communication gap nobody planned for. When something went wrong, there was usually a responsible party identifiable within the system.

 

Generative AI and voice assistants are disrupting this architecture in ways the food safety community has not yet fully reckoned with. Increasingly, both consumers and food industry professionals are bypassing traditional information channels and asking AI systems for food safety guidance directly. This is not a marginal behaviour. According to data from multiple technology research sources, AI-generated search summaries now appear at the top of results for a significant proportion of food-related queries in major markets, and voice assistants handle tens of millions of food-related questions per day globally.

The International Association for Food Protection (IAFP) 2025 Annual Meeting dedicated a full symposium to cutting through the hype of AI in food safety, and the concerns raised by speakers from Chick-fil-A, Ecolab, and the FDA were instructive[2]. David Monk of Chick-fil-A explicitly warned about hallucinations in large language models, the phenomenon where AI models generate plausible but factually fabricated information, and stressed the irreplaceable need for human oversight. Amani Babekir of Ecolab reinforced this directly: AI, she noted, will not eliminate the need for subject matter experts[2]. These are practitioners speaking from real deployment experience, not theoretical concerns.

 

The 2025 FAO report made the same point with institutional weight, explicitly identifying AI hallucinations, where models generate plausible but fabricated information, as a core risk, and warning that premature use of AI in food safety, whether by applying unsuitable techniques or implementing AI without the expertise to interpret its outputs, risks undermining the trust and credibility of the organisations employing it[1].

 

Root Causes

To address the problem properly, it is necessary to understand why it exists rather than simply cataloguing its symptoms. The root causes are structural, and they operate at multiple levels simultaneously.

 

Training Data Quality and Recency

Large language models and generative AI systems are trained on datasets assembled from the internet, scientific publications, regulatory documents, and other text sources. Food safety regulation is a domain characterised by frequent revision. Codex Alimentarius updates maximum residue limits. National regulatory bodies revise allergen thresholds. Recall databases are updated in real time. An AI model trained on data from eighteen months ago may confidently provide guidance based on superseded standards, and the model itself has no awareness of such a limitation, where it does not know what it does not know. The International AI Safety Report 2026 noted explicitly that AI systems can generate non-existent citations, biographies, or facts due to the hallucination phenomenon, with confidence indistinguishable from accurate information[3].

 

Confidence Calibration and the Absence of Uncertainty Signals

Human food safety experts communicate uncertainty. A microbiologist asked about the safety of a novel fermentation process, will hedge, qualify, and direct the questioner to primary sources. Generative AI systems are optimised, in many deployment contexts, to produce fluent and complete-sounding responses. The very qualities that make them engaging as interfaces, conversational fluency, apparent confidence, and absence of hesitation, are precisely the qualities that make them dangerous in high-stakes informational contexts. A consumer asking whether their food is safe to eat needs not just an answer, but an appropriately calibrated signal about how certain that answer is, where current consumer-facing AI systems are structurally poor at delivering it.

 

Regulatory Fragmentation and Jurisdictional Ambiguity

Food safety regulation is deeply jurisdictional, where the maximum level for aflatoxin B1 in cereals intended for direct human consumption is 2 μg/kg in the European Union and 20 μg/kg in the United States, for example, thus a tenfold difference that reflects different risk assessment methodologies and policy choices, not a factual disagreement about toxicology. An AI system that does not know the user's jurisdiction, or that defaults to one regulatory context when the user is operating in another, can deliver technically accurate information for the wrong regulatory environment. In a world where food businesses increasingly operate across multiple jurisdictions, and consumers travel internationally, this is not a minor edge case.

 

Biased and Unrepresentative Training Corpora

A further structural problem is that the training data for general-purpose AI models is heavily skewed toward high-resource, English-language, Western regulatory contexts. Food safety guidance for ASEAN markets, African regulatory frameworks, or small island developing states is systematically underrepresented. A food safety manager in Indonesia, Ghana, or Samoa who queries an AI system in English is likely to receive responses calibrated to FDA or EFSA standards, which may be entirely inapplicable to their regulatory environment and local food production context. The 2025 FAO report noted that data gaps are particularly pronounced for low- and middle-income countries[1], and such gaps translate directly into AI advice that is geographically and contextually unreliable.

 

Accountability Gaps in the Information Chain

Traditional food safety communication is embedded in accountability structures. A food safety consultant who provides incorrect advice can face professional and legal consequences. A regulatory body that publishes incorrect guidance is accountable to its mandate and subject to legislative oversight. An AI system that provides incorrect food safety advice sits outside virtually all of these accountability frameworks, because there is no licensing body for AI food safety advisors, and there is no professional indemnity requirement either. Hence, there is no systematic post-market surveillance of AI-generated food safety information analogous to the adverse event reporting systems that govern medical devices and pharmaceuticals, where such an accountability gap is not a minor regulatory oversight, which is a structural vulnerability that the food safety governance community has barely begun to address.

 

Real Power of AI

A critical analysis must acknowledge genuine achievement alongside genuine risk, and AI in food safety has genuine achievements worth examining carefully, because they also illuminate where the risks concentrate.

 

The FDA has deployed a boosted-tree machine learning model, which is specifically LightGBM, to predict the probability that an imported food shipment will violate regulatory requirements. By combining data on shipment history, product characteristics, and exporting establishment and country risk indicators, the model improves targeting efficiency and increases the likelihood of intercepting unsafe products at the border. This is a well-designed application of AI: it operates within a domain where the training data is well-defined, the outcome is measurable, the model's predictions are reviewed by human inspectors before action is taken, and the consequences of error are caught by subsequent verification steps rather than transmitted directly to end users.

 

Similarly, AI-enabled computer vision systems in food manufacturing environments, by detecting contaminants, verifying packaging integrity, and monitoring temperature compliance in real time, represent applications where AI augments human inspection capacity in controlled, verifiable, high-frequency tasks. The model's outputs are checked against physical reality continuously, errors are corrected in the production flow, and the system operates under the supervision of qualified food safety professionals.

 

In food safety more broadly, AI enables predictive risk modelling, rapid contaminant detection, smart surveillance systems, and blockchain-based traceability, all within contexts where expert human oversight is embedded in the workflow. The pattern that distinguishes good AI deployment from risky AI deployment in food safety is clear: human expertise in the loop, measurable and verifiable outcomes, appropriate uncertainty communication, and domain-specific training data of known quality.

 

The risk concentrates precisely where these conditions are absent, and consumer-facing AI communication, where the advice goes directly to an end user without expert intermediation, is the domain where most of these conditions are missing.

 

Mitigation Strategies

The answer to the question "Can we trust machine-generated food advice?" is not a binary yes or no, which is a conditional answer that depends on context, deployment design, governance, and user literacy. The following mitigation strategies reflect the current state of knowledge and are graded by feasibility and urgency.

 

Domain-Specific AI Systems with Curated, Versioned Knowledge Bases

The most direct technical mitigation is to develop food safety AI applications that do not rely on general-purpose large language models trained on undifferentiated internet data, but instead use curated, jurisdictionally specific, version-controlled knowledge bases. Regulatory databases, Codex Alimentarius texts, and national food standards can be structured as retrieval-augmented generation (RAG) systems, where the AI's outputs are anchored to specific, dated regulatory documents rather than statistical generalisations from training data. This architecture allows the system to say "this answer is based on EU Regulation 2023/XXX, which was current as of this date" rather than generating a confident response from poorly attributed training data. Such systems are technically feasible now, and several national food safety authorities are beginning to pilot them.

 

Mandatory Uncertainty Communication and Source Attribution

AI systems deployed in food safety communication contexts should be required to communicate uncertainty explicitly and to attribute their responses to specific sources. Thus, the choice is not merely a technical design choice, but it should be a regulatory requirement for any AI system that provides food safety guidance in a commercial or public health context. The analogy is nutritional labelling, where it must be such that the food manufacturers are required to declare what is in their product; AI food safety systems should be required to declare the basis and confidence level of their recommendations. The 2026 International AI Safety Report's documentation of AI hallucination risks provides the public health rationale for making this a regulatory rather than voluntary standard[3].

 

Regulatory Frameworks for AI-Generated Food Safety Information

There is currently no coherent international regulatory framework governing AI-generated food safety advice, which is a major gap that Codex Alimentarius, the FAO, and national food safety authorities need to address with some urgency. The framework does not need to be restrictive, but it needs to be clear. At minimum, it should establish that AI systems providing food safety guidance must meet defined accuracy standards, disclose their training data provenance and recency, provide source attribution, communicate uncertainty, and be subject to post-market surveillance for accuracy. The EU AI Act's risk-based classification framework provides one possible model, though its application to food safety communication specifically remains underdeveloped. The International AI Safety Report 2026 notes the importance of expert human oversight as a mitigation for AI hallucination risks[3], and regulatory frameworks should embed this requirement structurally.

 

Organisational AI Literacy in Food Businesses

AI has the potential to improve food safety training and communication, but communication can be hindered by various forms of noise, including channel limitations, time pressure, and message complexity. Food businesses that are deploying AI tools for internal food safety management, whether for HACCP documentation, supplier audit management, or staff training, need to invest in AI literacy as a food safety competency. Thus, training food safety teams to understand what AI systems can and cannot reliably do, to verify AI-generated regulatory information against primary sources, and to recognise the signs of hallucinated or outdated content is an upcoming requirement as it will safeguard the industry while expanding QA staff capabilities to adapt to new changes in the manufacturing sector. ISO 22000:2018's requirement for competence and awareness (Clause 7.2 and 7.3) provides an existing framework within which AI literacy can and should be situated.

 

Industry-Regulator Collaboration on Validation Standards

The FAO report identifies three core areas of AI deployment in food safety: a) scientific advice, b) inspection and border control, and c) operational activities of food safety competent authorities. For each of these domains, validation standards analogous to method validation requirements for laboratory testing need to be developed. An AI system making predictions about import violation probability should be evaluated against documented accuracy metrics, tested across diverse shipment types and origins, and revalidated when the model or its training data changes. These validation requirements do not yet exist in a standardised form, and developing them is a concrete, achievable step that the food safety community can take in the near term.

 

The Unanswered Questions

It is important to be honest about what we do not yet know, because the honest answer to several critical questions is that nobody knows yet, and that uncertainty itself should inform how cautiously we proceed.

 

We do not know, at a population level, how frequently AI-generated food safety advice is wrong, or how frequently those errors have health consequences. The surveillance infrastructure to detect AI-related food safety misinformation at a population level simply does not exist. We do not know what the threshold of public trust in AI food safety advice is, at what point a pattern of notable errors would cause consumers to discount AI guidance in ways that might themselves create safety risks (for instance, by causing people to distrust correct AI advice about a genuine food safety hazard). We do not know whether the regulatory frameworks being developed by the EU, FDA, and others will evolve quickly enough to address the rate at which AI capabilities and deployments are changing.

 

The 2025 FAO and Wageningen report notes that the risk of prematurely using AI in food safety, whether by applying techniques that are not yet suitable for the specific data or problem, or by implementing AI without the necessary expertise to interpret its output, lies in potentially undermining the trust and credibility of the organisation employing it, which is well-stated. But it frames the risk at the organisational level. The deeper risk, as AI-generated food safety advice reaches consumers directly through voice assistants and AI summary features in search engines, is that it undermines public trust in food safety guidance more broadly, including the authoritative guidance that comes from regulatory bodies and certified professionals who have earned that trust over decades.

 

Conclusion

Artificial intelligence is not going to stop being applied to food safety, and nor should it. The genuine benefits, in pathogen detection speed, improve surveillance efficiency, supply chain traceability, and predictive risk modelling are real, documented, and important. The food safety community's task is not to resist AI adoption, but to shape it to demand well-designed systems, appropriate governance, embedded human expertise, and honest communication about the limits of what AI can reliably know.

 

The analogy that feels most apt is the introduction of rapid microbiological testing methods into food safety laboratories in the 1990s and 2000s. Those methods were faster, cheaper, and more scalable than traditional culture methods, and they required new validation standards, new competency requirements, and new quality assurance frameworks before they could be trusted. The industry built those frameworks, and rapid methods are now a cornerstone of modern food safety. AI can follow a similar trajectory, but only if the governance work keeps pace with the technology deployment, and in the current context, it is not.

 

Food safety professionals should be curious about AI, and should use it where it has been validated, verified its outputs against primary sources, and should invest in the AI literacy of their food safety teams. The regulators should realize that the window for proactive governance is narrowing, while consumers should treat AI food safety advice as a starting point for a question, not an endpoint for a decision. Thus, the question is not whether to trust AI in food safety, but rather how to build the conditions under which that trust is justified.

 

 

References

[1] van Meer, F., van der Velden, B. & Takeuchi, M. 2025. Artificial Intelligence for Food Safety – A Literature Synthesis, Real-World Applications and Regulatory Frameworks. FAO & Wageningen Food Safety Research. https://www.fao.org/food-safety/news/news-details/en/c/1748997/

[2] New Food Magazine. (2025, July 30). AI in food safety: real-world solutions from IAFP 2025. https://www.newfoodmagazine.com/news/253921/ai-in-food-safety-iafp-2025/

[3] International AI Safety Report 2026. International Scientific Report on the Safety of Advanced AI. https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026

[4] Food Safety Magazine. (2025, October 31). FAO Report Highlights Needs for Responsible AI Adoption in Food Safety Fields. https://www.food-safety.com/articles/10845-fao-report-highlights-needs-for-responsible-ai-adoption-in-food-safety-fields

[5] ScienceDirect. (2025, April). Advancing food safety behavior with AI: Innovations and opportunities in the food manufacturing sector. https://www.sciencedirect.com/science/article/pii/S0924224425001864

[6] Academia.edu / Open Access. (2025). Artificial intelligence in food safety and nutrition practices: opportunities and risks. https://www.academia.edu/3067-1345/2/3/10.20935/AcadNutr7904

[7] ScienceDirect. (2025, September). Food safety – the transition to artificial intelligence (AI) modus operandi. https://www.sciencedirect.com/science/article/pii/S0924224425004145

[8] ScienceDirect. (2025, September). Harnessing Artificial Intelligence to Safeguard Food Quality and Safety. https://www.sciencedirect.com/science/article/pii/S0362028X25001735

[9] Food Safety Magazine. (2026, March). Leveraging AI for Food Safety Without Becoming Its Victim. https://www.food-safety.com/articles/11280-leveraging-ai-for-food-safety-without-becoming-its-victim

[10] Exploration Publishing. (2025, October). The role of artificial intelligence (AI) in foodborne disease prevention and management—a mini literature review. https://www.explorationpub.com/Journals/edht/Article/101167