@inproceedings{b279bc71d31c48fd8d068f5b4565e9ba,
title = "From Explanation to Action: A Case Study of Agronomist Workflows",
abstract = "Agronomists bear the responsibility of verifying AI predictions to ensure safe agricultural decision-making. However, a critical gap persists in understanding how these domain experts leverage Explainable AI (XAI) to verify diagnoses within their workflows. We conducted a qualitative case study with nine professional agronomists to evaluate feature-attribution and example-based explanations. Our observations suggest that abstract heatmaps risk reinforcing automation bias by obscuring the biological ground truth. In contrast, example-based explanations appeared to support the experts' epistemic practice of situated seeing. Furthermore, participants consistently prioritized actionability over transparency. They viewed the diagnosis not as an endpoint, but as a prerequisite for intervention. Consequently, this work contributes design considerations to bridge the gap between static model explanations and active agronomic scrutiny.",
keywords = "Agronomy, Digital Agriculture, Domain Experts, Explainable AI, Human-centered AI, Qualitative Study",
author = "Porf{\'i}rio, \{Rui Pedro\} and Santos, \{Pedro Albuquerque\} and Madeira, \{Rui Neves\}",
note = "Publisher Copyright: {\textcopyright} 2026 Copyright held by the owner/author(s).; Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 ; Conference date: 13-04-2026 Through 17-04-2026",
year = "2026",
month = apr,
day = "13",
doi = "10.1145/3772363.3799333",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "ACM - Association for Computing Machinery",
editor = "Nuria Oliver and Shamma, \{David A.\} and Heloisa Candello and Pablo Cesar and Pedro Lopes and Valentino Artizzu and Fiona Draxler and Gustavo Lopez and Reinschluessel, \{Anke V.\} and Xin Tong and Dugas, \{Phoebe O. Toups\}",
booktitle = "CHI EA '26",
address = "United States",
}