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Artificial intelligence is increasingly being embedded in agri-food supply chains through applications such as automated quality grading, risk scoring, demand forecasting, logistics optimisation, pricing analytics, and food safety alerts. These systems promise efficiency, resilience, and improved decision support, but they also reshape how decisions are made and who is affected by them. In food systems, AI governance is not only a technical issue but also a matter of safety, fairness, market access, and public trust. Existing responsible AI principles provide important normative foundations, including fairness, transparency, accountability, human oversight, and social benefit. However, these principles are often framed at a general level and do not fully address the distinctive structure of agri-food supply chains. Food chains are fragmented across farms, processors, logistics providers, retailers, technology platforms, regulators, and consumers, which makes governance more complex than in single-organisation AI deployment. This article develops a domain-specific Responsible AI Governance Framework for agri-food supply chains. The framework integrates four pillars: transparency, accountability, traceability, and human oversight. It argues that responsible AI in food must make algorithmic decisions visible, assign responsibility across distributed actors, preserve auditable records, and ensure meaningful human safeguards for high-risk decisions. The proposed framework specifies transparency requirements for different AI applications, maps accountability responsibilities across supply chain actors, connects traceability systems to auditability, and defines human oversight mechanisms for safety-critical and livelihood-sensitive decisions. Three tables operationalise the framework through transparency requirements, accountability allocation, and integrated governance mechanisms. The article concludes that responsible AI in food requires governance that is as distributed, interconnected, and safety-conscious as the supply chains it seeks to guide.

Food systems are increasingly embedding algorithmic traceability systems that combine blockchain infrastructures, AI-supported verification, IoT sensors, QR-enabled disclosure, and digital chain-of-custody tools. These systems are commonly promoted as instruments of transparency, fraud prevention, and consumer reassurance. Their deeper theoretical significance, however, lies in the way they reorganise the social foundations through which food becomes trusted. Existing approaches to food trust often emphasise interpersonal confidence, brand reputation, certification labels, and institutional credibility. Yet these approaches do not fully explain what happens when trust claims are mediated by automated records, distributed ledgers, sensor-generated evidence, and platform-based verification interfaces. The central problem addressed in this article is whether algorithmic traceability merely increases information or whether it changes the nature of food trust itself. This article develops an original theory of algorithmic food trust transformation. It argues that algorithmic traceability shifts food trust from a relational and reputational regime toward a system-mediated regime organised around visibility, verification, and institutional confidence. The article does not report new empirical data; instead, it builds theory by synthesising research on food trust, traceability, blockchain, digital transparency, certification, consumer behaviour, and food governance. The proposed theory identifies three sequential mechanisms. Algorithmic visibility makes selected aspects of the supply chain seeable; verification converts food claims into auditable assertions; and institutional confidence encourages consumers and market actors to rely on the functioning of technical and regulatory systems rather than on direct personal knowledge. These mechanisms do not eliminate trust, but they relocate it from persons and brands toward infrastructures, platforms, certifiers, and governance arrangements. The article contributes to food sociology and trust studies by showing that algorithmic traceability is not simply a technical improvement in supply chain information. It rewires the foundations of food trust and creates new vulnerabilities linked to data integrity, institutional legitimacy, platform control, and the opacity of verification systems. The theory therefore calls for a more critical understanding of digital food trust as a sociotechnical achievement rather than a natural consequence of transparency.

Food system transparency has become a defining demand of contemporary food governance as consumers, regulators, producers, retailers, and civil society seek clearer information about food safety, sustainability, provenance, and accountability. Yet transparency is still frequently reduced to traceability, understood as the capacity to follow a product through production, processing, distribution, and retail. Traceability remains essential because it can support food safety, provenance verification, recall management, and supply-chain visibility. However, it offers only a partial view of the decisions, data practices, and governance arrangements that increasingly shape food systems. The central problem is that traceability can show where food has been without explaining why food-system decisions are made, who benefits from data use, how algorithmic judgements are produced, or how affected stakeholders can question decisions. As artificial intelligence, digital platforms, automated decision support, blockchain systems, and data-intensive infrastructures become more embedded in food systems, transparency must include decision logic as well as product movement. A narrow traceability model may increase visibility while leaving power asymmetries, accountability gaps, and ethical risks unresolved. Transparency therefore needs to be reframed as a broader socio-technical condition. This Perspective Article argues that food system transparency must move beyond conventional traceability toward explainable, participatory, and ethics-centred AI. Explainable AI can make algorithmic decisions more understandable to producers, regulators, firms, consumers, and affected communities. Participatory design can ensure that transparency systems reflect stakeholder needs, rights, and lived realities rather than only institutional or commercial priorities. Ethics-centred governance can connect technical transparency with fairness, accountability, privacy, contestability, and public trust. The article contributes a conceptual framework for reimagining food system transparency as understandable, contestable, inclusive, accountable, and sustainability-oriented. It positions AI not merely as a tool for monitoring food supply chains but also as a governance object that must itself be explained, scrutinised, and democratically shaped. The proposed perspective highlights practical implications for food businesses, technology developers, policymakers, regulators, and civil society organisations. It concludes that transparent AI can support more trustworthy food systems only when traceability is integrated with explanation, participation, ethical responsibility, and accountable decision-making.