ALGORITHMIC FOOD SECURITY, AGRICULTURAL DATA GOVERNANCE, AND SMALLHOLDER INCLUSION: A GOVERNED CAPABILITY-CONVERSION FRAMEWORK
DOI:
https://doi.org/10.35631/AIJBES.829108Keywords:
Agricultural Artificial Intelligence, Algorithmic Food Security, Conversion Freedom, Data Governance, Smallholder Inclusion, Livelihood Capabilities, Algorithmic DependencyAbstract
Artificial intelligence (AI) is transforming agriculture, yet its contribution to smallholder food security depends on more than just access to or adoption of technology. This conceptual paper develops a governed capability-conversion framework explaining how AI-mediated opportunities translate into multidimensional household food security. Drawing on an integrative, theory-driven review, the framework explains how AI and data conditions interact with governance and substantive inclusion to shape livelihood capabilities and, ultimately, multidimensional household food security. It integrates the Capability Approach, Data Justice, and Resource Dependence Theory through conversion freedom, defined as smallholders’ substantive institutional capacity to transform algorithmically mediated opportunities into livelihood capabilities while retaining meaningful agency and viable alternatives. The framework distinguishes a governed capability-conversion pathway from a dependency-constrained pathway and identifies enabling data-governance quality and algorithmic dependency as opposing moderators. The framework advances agricultural AI scholarship by explaining why comparable digital participation can yield divergent developmental outcomes and by providing a testable foundation for future empirical research.
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