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dc.contributor.authorTzachor, A.
dc.contributor.authorDevare, M.
dc.contributor.authorKing, B.
dc.contributor.authorAvin, S.
dc.contributor.authorÓ hÉigeartaigh, S.
dc.date.accessioned2022-05-23T12:18:17Z
dc.date.available2022-05-23T12:18:17Z
dc.date.issued2022
dc.identifier.citationTzachor, A., Devare, M., King, B., Avin, S. & Ó hÉigeartaigh, S. (2022). Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nature Machine Intelligence, 4(2), 104-109.
dc.identifier.issn2522-5839
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7475
dc.description.abstractGlobal agriculture is poised to benefit from the rapid advance and diffusion of artificial intelligence (AI) technologies. AI in agriculture could improve crop management and agricultural productivity through plant phenotyping, rapid diagnosis of plant disease, efficient application of agrochemicals and assistance for growers with location-relevant agronomic advice. However, the ramifications of machine learning (ML) models, expert systems and autonomous machines for farms, farmers and food security are poorly understood and under-appreciated. Here, we consider systemic risk factors of AI in agriculture. Namely, we review risks relating to interoperability, reliability and relevance of agricultural data, unintended socio-ecological consequences resulting from ML models optimized for yields, and safety and security concerns associated with deployment of ML platforms at scale. As a response, we suggest risk-mitigation measures, including inviting rural anthropologists and applied ecologists into the technology design process, applying frameworks for responsible and human-centred innovation, setting data cooperatives for improved data transparency and ownership rights, and initial deployment of agricultural AI in digital sandboxes.
dc.description.sponsorshipTempleton World Charity Foundation
dc.format.extent104-109
dc.language.isoen
dc.subjectAgriculture
dc.subjectArtificial Intelligence
dc.subjectRisk Factors
dc.subjectMachine Learning
dc.subjectData
dc.subjectRisk Reduction
dc.subjectParticipatory Research
dc.titleResponsible artificial intelligence in agriculture requires systemic understanding of risks and externalities
dc.typeJournal Article
cg.contributor.affiliationUniversity of Cambridge
cg.contributor.affiliationReichman University, Israel
cg.contributor.affiliationPlatform for Big Data in Agriculture
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.hubHeadquarters and Western Africa Hub
cg.identifier.bibtexciteidTZACHOR:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.journalNature Machine Intelligence
cg.notesPublished online: 23 Feb 2022
cg.accessibilitystatusLimited Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCopyrighted; all rights reserved
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1038/s42256-022-00440-4
cg.iitaauthor.identifierMedha Devare: 0000-0003-0041-4812
cg.futureupdate.requiredNo
cg.identifier.issue2
cg.identifier.volume4


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