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From Explanation to Action

2026-04-13Documento de conferência Acesso aberto
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dc.contributor.authorPorfírio, Rui Pedro
dc.contributor.authorSantos, Pedro Albuquerque
dc.contributor.authorMadeira, Rui Neves
dc.contributor.institutionNOVALincs
dc.date.accessioned2026-07-09T10:19:01Z
dc.date.available2026-07-09T10:19:01Z
dc.date.issued2026-04-13
dc.descriptionPublisher Copyright: © 2026 Copyright held by the owner/author(s).
dc.description.abstractAgronomists 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent5
dc.format.extent555112
dc.identifier.doi10.1145/3772363.3799333
dc.identifier.isbn9798400722813
dc.identifier.otherPURE: 163664836
dc.identifier.otherPURE UUID: b279bc71-d31c-48fd-8d06-8f5b4565e9ba
dc.identifier.otherScopus: 105038086025
dc.identifier.urihttp://hdl.handle.net/10362/204400
dc.identifier.urlhttps://www.scopus.com/pages/publications/105038086025
dc.language.isoeng
dc.peerreviewedyes
dc.publisherACM - Association for Computing Machinery
dc.subjectAgronomy
dc.subjectDigital Agriculture
dc.subjectDomain Experts
dc.subjectExplainable AI
dc.subjectHuman-centered AI
dc.subjectQualitative Study
dc.subjectHuman-Computer Interaction
dc.subjectComputer Graphics and Computer-Aided Design
dc.subjectSoftware
dc.titleFrom Explanation to Actionen
dc.title.subtitleA Case Study of Agronomist Workflowsen
dc.typeconference object
degois.publication.titleCHI EA '26
degois.publication.titleExtended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026
dspace.entity.typePublication
rcaap.rightsopenAccess

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