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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.

Artificial intelligence is increasingly used in food safety systems to support contamination detection, shelf-life prediction, quality classification, inspection prioritisation, and risk scoring. These applications promise faster, more sensitive, and more scalable decision support across complex food supply chains. However, the same predictive complexity that makes AI useful can also make its outputs difficult for food safety professionals, regulators, and operators to interpret. The central problem addressed in this framework article is that AI-supported food safety decisions may become opaque, uncontestable, and weakly aligned with existing structures of regulatory responsibility. When risk scores or alerts cannot be understood, challenged, or traced to relevant evidence, they may undermine rather than strengthen food safety governance. This problem is especially serious because food safety decisions involve direct public health consequences, market consequences, and legal accountability. The objective of this article is to develop an original Explainable AI Framework tailored to food safety decision systems. The framework integrates four interdependent dimensions: risk interpretation, regulatory confidence, operational accountability, and human review. It treats explainability not only as a technical property of models but also as a governance condition for responsible food safety decision-making. The article is based on a conceptual synthesis of 27 peer-reviewed articles published between 2017 and 2025 across food safety, explainable artificial intelligence, algorithmic accountability, risk assessment, and digital food safety systems. It does not present new empirical data. Instead, it constructs a design and governance framework that can guide the development, validation, deployment, and oversight of AI-supported food safety tools. The proposed framework defines four pillars: interpretable risk communication, transparent regulatory evidence, accountable operational responsibility, and contestable human review. Together, these pillars show how food safety AI systems can become not only predictive but also auditable, trustworthy, and practically usable. The article argues that embedding explainability into food safety AI is a governance and trust-building imperative rather than a purely technical enhancement.

Artificial intelligence is increasingly embedded in agricultural decision support, farm platforms, automation, sensing, advisory systems, and data-driven management. These systems influence how farms interpret risk, allocate inputs, monitor environments, plan production, and respond to market and ecological uncertainty. As agricultural AI becomes more consequential, its governance must address not only technical performance but also the social conditions under which digital decisions are made, trusted, challenged, and revised. Agricultural AI governance is often framed around technical accuracy, transparency, regulatory compliance, or institutional oversight. While these are necessary, they are insufficient when farmers have limited influence over system design, data practices, model assumptions, recommendation logic, or dispute pathways. Governance weaknesses can produce exclusion, loss of autonomy, data misuse, algorithmic opacity, dependency on platforms, and digital decisions that affected users cannot meaningfully challenge. This article develops a participatory AI governance framework for agriculture centred on farmer voice, local knowledge integration, contestable digital decisions, and accountability safeguards. The framework treats governance as a socio-technical practice that connects design, deployment, monitoring, revision, and redress. It argues that responsible agricultural AI requires institutional mechanisms through which farmers and other affected actors can shape, interpret, evaluate, contest, and improve digital systems. The framework contributes to agricultural AI governance by showing how participation can strengthen legitimacy, trust, accountability, and contextual fit. It positions farmer voice as a condition of decision legitimacy, local knowledge as a condition of contextual accuracy, contestability as a condition of procedural fairness, and accountability safeguards as a condition of responsible institutional performance. Participatory governance is therefore presented as a practical foundation for agricultural AI that is more locally grounded, explainable, responsive, and accountable.