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Cited by 84Ethics and Social Impacts of AISmart Agriculture and AIArtificial Intelligence Applications

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Who is responsible for ‘responsible AI’?: Navigating challenges to build trust in AI agriculture and food system technology

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Alexander, Carrie S., Yarborough, Mark, Smith, Aaron·2024·Precision Agriculture
DOI 10.1007/s11119-023-10063-3
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Abstract

This article presents findings from interviews that were conducted with agriculture and food system researchers to understand their views about what it means to conduct ‘responsible’ or ‘trustworthy’ artificial intelligence (AI) research. Findings are organized into four themes: (1) data access and related ethical problems; (2) regulations and their impact on AI food system technology research; (3) barriers to the development and adoption of AI-based food system technologies; and (4) bridges of trust that researchers feel are important in overcoming the barriers they identified. All four themes reveal gray areas and contradictions that make it challenging for academic researchers to earn the trust of farmers and food producers. At the same time, this trust is foundational to research that would contribute to the development of high-quality AI technologies. Factors such as increasing regulations and worsening environmental conditions are stressing agricultural systems and are opening windows of opportunity for technological solutions. However, the dysfunctional process of technology development and adoption revealed in these interviews threatens to close these windows prematurely. Insights from these interviews can support governments and institutions in developing policies that will keep the windows open by helping to bridge divides between interests and supporting the development of technologies that deserve to be called “responsible” or “trustworthy” AI.

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  • Transparency promotes accountability by enabling oversight and ensuring that systems are developed and deployed in ways that respect societal values.

    “Transparency promotes accountability by enabling oversight and ensuring that systems are developed and deployed in ways that respect societal values [7, 34].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [7]

    The Discussion (page not provided) links transparency with oversight and considers how competing interests and ethical standards might be brought into alignment. The Introduction (page not provided) calls for shared responsibility during technology development and deployment. The paper does not establish that transparency itself promotes accountability or ensures that systems respect societal values. (LLM, conf=0.78)

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Metadata

Authors
Alexander, C. S., Yarborough, M., & Smith, A.
Affiliations
University of California, Davis (US)
Corresponding author
  • Alexander, Carrie S.
  • Yarborough, Mark
ORCID
Venue
Precision Agriculture
Publisher
Springer Science and Business Media LLC
Published
2023-08-29
Volume
25
Issue
1
Pages
146-185
ISSN
  • 1385-2256 (ISSN-L)
  • 1573-1618
DOI
10.1007/s11119-023-10063-3
OpenAlex
W4386252066
Cited by
84
References
4
Citations by year
2026: 38 · 2025: 33 · 2024: 11 · 2023: 2
FWCI
20.97
Type
journal article
Language
en
Open access
Licence
Locations
Precision Agriculture (publishedVersion, cc-by)
Funders
National Institute of Food and Agriculture (2020-67021-32855/project accession no. 1024262)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
responsible AI, trustworthy AI, artificial intelligence in agriculture, trust in AI, AI regulation, technology adoption barriers, farmer trust, AI policy, agricultural research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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