Artificial Intelligence and Predictive Food Insecurity: A Conceptual Framework for Ethical Transparency and Trust (BTT)

Abstract

This theoretical study introduces the Bias–Transparency–Trust (BTT) framework, a conceptual model for examining the ethical dimensions of artificial intelligence (AI) in predictive food-security systems across developing countries. As organizations such as FAO and WFP increasingly employ machine learning to anticipate hunger crises, ethical challenges surrounding data bias, opacity, and public trust remain insufficiently explored. The BTT framework proposes that ethical legitimacy in AI-based foresight arises only when bias mitigation, transparent communication, and stakeholder trust function in equilibrium. Drawing from theories of data justice, anticipatory governance, and communicative ethics, the paper conceptualizes AI as an ethical infrastructure, a moral architecture that shapes how societies envision and act upon the future. Through normative reasoning and conceptual synthesis, the study introduces the concept of predictive legitimacy, emphasizing the moral credibility of anticipatory decision-making. It argues that democratizing predictive governance requires participatory transparency, co-created data practices, and iterative trust-building mechanisms. The framework thus bridges technical and social ethics, offering policymakers and humanitarian agencies a relational model for responsible innovation. Ultimately, this study positions AI not merely as a forecasting tool, but as a vehicle for collective resilience and justice in global hunger governance.