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Harnessing discrete choice experiments to elicit preferred configurations of trustworthy AI augmented decision
Asim Zia1,2, Maaz Gardezi3, Xinjing Yu3
1Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States.
Introduction:
The increase in Artificial Intelligence (AI) and sensor data driven Precision Agriculture (PA) technologies show promise to improve efficiencies in agricultural production systems and decrease adverse impacts of agriculture on environment compared to traditional approaches. Yet, complex trade-offs (e.g. cost, accuracy, precision and data ownership) in the design and configuration of trustworthy AI augmented decision support systems (AI-DSS) for advancing responsible and ethical PA have surfaced. This study harnesses Discrete Choice Experiments (DCEs) to elicit stated preferences of Certified Crop Advisors (CCAs) for informing the design and configurations of trustworthy AI-DSS. The research is guided by two questions and eight associated hypotheses: (a) How do cost, accuracy, precision, and data ownership influence the preferences of CCAs for adopting AI-DSS in agriculture? (b) Which AI perceptions, PA technology concerns and prior DSS experience predict the adoption of AI-DSS configurations?.
Methods:
Six focus groups informed the design of the choice set, comparing low, medium and high cost AI-DSS with varying accuracy, precision and data ownership attributes. The survey was circulated by Crop Science Society of America to ~2600 CCAs with a lottery-based incentive, leading to 771 responses (response rate = 29.65%). The DCE data were analyzed using a Standard (McFadden) Logit Model, and a Random Utility Mixed Logit Model.
Results:
Analysis showed 25.54% of the participants opted out, and 45.36%, 19.23%, 9.85% prefer low, medium and high-cost AI-DSS, respectively. Marginal improvement of 1% accuracy leads to ~4% (p < 0.001) and spatial precision leads to ~ 1.5% (p < 0.01) increment in the likelihood of AI-DSS adoption.
Discussion:
AI perceptions, PA technology concerns and prior DSS experience significantly predict the variability in the adoption of three types of AI-DSS.
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