Integrating human-centered AI for land use policy: Insights from agricultural interventions in international development

Policymakers often struggle with information overload from vast technical documentation, hindering effective evidence-based decision-making. This study explores how a human-centered artificial intelligence model was fine-tuned to analyze agricultural interventions within international development projects, providing a methodological foundation to support the synthesis of complex evidence for more informed land use policy formulation. Engaging domain experts and incorporating human expertise, we developed a taxonomy of land use practices—such as water resource management, land use planning, and agronomic practices—that reflects the nuanced realities of local interventions. By integrating this human-centered taxonomy into the model's training, we ensured that the artificial intelligence system efficiently identified and categorized interventions in a way that upholds humanistic practices and aligns with the needs of policymakers and communities. Our findings demonstrate that this approach enhances the analysis of land use interventions. The model proved to be both scalable and cost-effective, analyzing large volumes of data more rapidly than traditional human analysis. These results underscore the potential of human-centered artificial intelligence in transforming land use policymaking by empowering stakeholders with faster and more accurate data synthesis. This methodological approach has the potential to support policymakers in synthesizing evidence more efficiently, which could ultimately lead to more informed and effective land use policies and improved outcomes in international development.