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Paper short abstract

In this paper, we examine priorities in AI research for agriculture as revealed in publications and patents, against societal demands on agriculture as expressed in legal, policy and social documents.

Paper long abstract

Large investments in artificial intelligence (AI) are often justified by their potential contribution to the Sustainable Development Goals (SDGs). However, research and innovation rarely benefit all parts of society equally. This paper examines the public values of research in the context of AI applications in agriculture. Two dimensions are central: alignment, referring to the extent to which the distribution of research topics corresponds to the distribution of societal demands or needs, and appropriateness, referring to whether research outputs can effectively address those needs within specific socio-technical contexts. Focusing on AI-enabled agricultural innovation, we ask: who benefits from advances in AI for agriculture? To address this question, we develop a framework that compares the supply of research with the distribution of societal demands. Research priorities are measured through publications, patents, using topic modelling and domain thesauri (CABI, AGROVOC). Societal needs are derived from policy documents (FAOlex, Overton), news and policy debates, and social media signals. Appropriateness is assessed by linking AI research topics to contextual factors such as crops, land use, climate conditions, infrastructure, and data availability. By comparing the distribution of AI-agriculture research with policy priorities and needs expressed by actors such as farmers, policymakers, and the public, the study maps how the benefits of AI research are distributed across countries and social groups. The paper contributes to the development of quantitative tools that can help in making visible the (mis)alignment between research priorities in emerging technologies and societal demands.