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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 systems increasingly generate consumer data through digital platforms, shopping records, dietary applications, wearable technologies, loyalty schemes, and personalised nutrition services. These data can reveal patterns of preference, dietary behaviour, product use, affordability constraints, and health-oriented needs. However, consumer data are often used for commercial targeting while remaining disconnected from product reformulation, nutrition improvement, and public health goals. AI-enabled food intelligence is frequently positioned as a tool for predicting consumer preferences or delivering personalised recommendations. This narrow use limits the potential of AI to support healthier food environments, more acceptable reformulated products, and more transparent consumer communication. A broader framework is needed to connect consumer insight with nutrition evidence, product innovation, ethical governance, and public trust. This article proposes a consumer-centred food intelligence framework linking AI, nutrition personalization, product reformulation, trust, transparency, and public health. The framework treats consumers not only as data sources but also as beneficiaries, participants, and evaluators of food-system innovation. It explains how individual and aggregated consumer intelligence can inform dietary advice, product design, reformulation priorities, and responsible communication. The article contributes a structured conceptual model for ethically transforming consumer data into health-oriented and trust-sensitive food-system decisions. It argues that AI should not only recommend diets or predict purchasing behaviour, but should also help translate nutritional evidence into acceptable products and transparent innovation pathways. Consumer-centred food intelligence is presented as a foundation for more responsible, healthier, and publicly trusted digital food systems.

Artificial intelligence is increasingly embedded in precision agriculture through sensing, remote observation, predictive modelling, automated classification, and decision-support applications. Technical performance alone, however, is insufficient for determining whether these systems are responsibly designed or appropriately used. This article proposes an original governance framework that connects agricultural context definition, farm-data governance, transparency, explainability, accountability, sustainability assessment, farmer participation, human oversight, risk safeguards, implementation-readiness review, monitoring, and adaptive feedback. The framework treats transparency as a requirement for documenting data provenance, data quality, model assumptions, system limitations, and decision pathways, while explainability is positioned as a task-specific means of supporting interpretation rather than proof of model validity. Accountability is structured through explicit allocation of responsibilities among developers, platform providers, data stewards, advisors, farmers, and oversight actors. Environmental, social, and economic sustainability are assessed alongside digital inclusion, usability, local relevance, farmer autonomy, and trust-building conditions. The framework also addresses algorithmic opacity, bias, model drift, poor local calibration, data misuse, cybersecurity, automation dependence, accountability gaps, and implementation overclaiming. Its principal contribution is a layered, adaptive governance model for responsible agricultural decision support. The framework supports governance planning and research design but does not establish empirical validity, legal compliance, policy acceptance, farmer approval, sustainability performance, trust, certification, or deployment readiness.