Who is responsible for ‘responsible AI’?: Navigating challenges to build trust in AI agriculture and food system technology

Carrie S Alexander, Mark Yarborough and Aaron Smith

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: This article presents findings from interviews that were conducted with agriculture and food system researchers to understand their views about what it means to conduct ‘responsible’ or ‘trustworthy’ artificial intelligence (AI) research. Findings are organized into four themes: (1) data access and related ethical problems; (2) regulations and their impact on AI food system technology research; (3) barriers to the development and adoption of AI-based food system technologies; and (4) bridges of trust that researchers feel are important in overcoming the barriers they identified. All four themes reveal gray areas and contradictions that make it challenging for academic researchers to earn the trust of farmers and food producers. At the same time, this trust is foundational to research that would contribute to the development of high-quality AI technologies. Factors such as increasing regulations and worsening environmental conditions are stressing agricultural systems and are opening windows of opportunity for technological solutions. However, the dysfunctional process of technology development and adoption revealed in these interviews threatens to close these windows prematurely. Insights from these interviews can support governments and institutions in developing policies that will keep the windows open by helping to bridge divides between interests and supporting the development of technologies that deserve to be called “responsible” or “trustworthy” AI.

Keywords: 30 Agricultural; Veterinary and Food Sciences (for-2020); 40 Engineering (for-2020); 3002 Agriculture; Land and Farm Management (for-2020); 3004 Crop and Pasture Production (for-2020); 4017 Mechanical Engineering (for-2020); Machine Learning and Artificial Intelligence (rcdc); 2 Zero Hunger (sdg); Artificial intelligence; Precision agriculture; AI ethics; Trustworthy AI; Food policy; Technology adoption; 0703 Crop and Pasture Production (for); Agronomy & Agriculture (science-metrix); 3002 Agriculture; land and farm management (for-2020); 3004 Crop and pasture production (for-2020); 4017 Mechanical engineering (for-2020) (search for similar items in EconPapers)
Date: 2024-02-01
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