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Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

Abstract

Global 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.

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Acknowledgements

This paper was made possible through the support of a grant from Templeton World Charity Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation.

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A.T., M.D., B.K., S.A. and S.Ó.H, developed the paper jointly and all contributed equally to the writing of the text.

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Correspondence to Asaf Tzachor or Seán Ó hÉigeartaigh.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Matthew McCabe and John Quinn for their contribution to the peer review of this work.

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Tzachor, A., Devare, M., King, B. et al. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat Mach Intell 4, 104–109 (2022). https://doi.org/10.1038/s42256-022-00440-4

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