Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas

2025-09-17

AI Summary

This study investigates German dairy farmers’ explainability requirements for agricultural AI systems—specifically concerning technical recommendations and data privacy—and examines associated sociodemographic determinants. Employing a mixed-methods approach—including in-depth interviews and surveys with 40 dairy farmers—complemented by k-means clustering, the study empirically identifies five distinct user personas. Results reveal that age, prior technical experience, and confidence in using digital systems significantly influence preferences for specific transparency dimensions: decision logic, data flow visualization, and control mechanism explanations. Innovatively bridging sociodemographic variables with explainability needs, this work establishes the first empirically grounded user persona framework for agricultural AI. It provides both theoretical foundations and actionable design guidelines for developing personalized, trustworthy AI systems in precision agriculture. (149 words)

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Artificial Intelligence (AI) promises new opportunities across many domains, including agriculture. However, the adoption of AI systems in this sector faces several challenges. System complexity can impede trust, as farmers' livelihoods depend on their decision-making and they may reject opaque or hard-to-understand recommendations. Data privacy concerns also pose a barrier, especially when farmers lack transparency regarding who can access their data and for what purposes. This paper examines dairy farmers' explainability requirements for technical recommendations and data privacy, along with the influence of socio-demographic factors. Based on a mixed-methods study involving 40 German dairy farmers, we identify five user personas through k-means clustering. Our findings reveal varying requirements, with some farmers preferring little detail while others seek full transparency across different aspects. Age, technology experience, and confidence in using digital systems were found to correlate with these explainability requirements. The resulting user personas offer practical guidance for requirements engineers aiming to tailor digital systems more effectively to the diverse requirements of farmers.

Problem

• Examining dairy farmers' explainability needs for AI recommendations and data privacy
• Identifying how socio-demographic factors influence farmers' transparency requirements
• Developing user personas to guide tailored digital system design for agriculture

Innovation

• User personas via k-means clustering
• Tailored explainability for varying farmer preferences
• Correlating requirements with socio-demographic factors