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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.

Artificial intelligence is increasingly used across agriculture and food systems for precision farming, yield prediction, pest detection, supply-chain optimisation, quality control, decision support, and resource management. These applications are often promoted as tools for increasing productivity, reducing uncertainty, improving resource efficiency, and strengthening food-system resilience. As AI becomes embedded in farms, supply chains, laboratories, and food governance infrastructures, its consequences extend beyond technical performance. The central problem is that the benefits of agri-food AI are frequently assessed through accuracy, automation efficiency, profitability, or optimisation metrics, while broader ethical, equity, environmental, and governance implications receive less systematic attention. AI systems may alter farmer autonomy, labour relations, data ownership, market participation, environmental decision-making, and institutional accountability. Treating these consequences as secondary risks understates the degree to which AI changes relationships among people, technologies, land, food, and power. This critical review examines responsible AI in agriculture and food systems through four connected dimensions: ethics, equity, sustainability, and governance. It argues that responsible AI cannot be reduced to technical reliability or model performance because agri-food systems are socially embedded, ecologically consequential, and institutionally fragmented. The article therefore evaluates responsible AI as a socio-technical and governance challenge rather than as a purely computational problem. The article contributes a critical argument that responsible agri-food AI requires accountable design, inclusive access, sustainability-aware evaluation, and multi-level governance. It emphasises that trustworthy AI in agriculture and food systems depends on who controls data, who benefits from automation, who bears environmental and social risks, and who participates in decision-making. Responsible AI is therefore positioned as a core condition for equitable, sustainable, and publicly legitimate agri-food transformation.