Growing algorithmic governmentality: Interrogating the social construction of trust in precision agriculture

Gardezi, Maaz; Stock, Ryan

doi:10.1016/j.jrurstud.2021.03.004

Cited by 40 publications

(32 citation statements)

References 61 publications

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“…This is very significant and may suggest a need to give a greater emphasis on sustainability within AI debates. In addition, trust has been well-documented as being an important principle within the agricultural sector, specifically, in relation to data-sharing and deploying emerging technologies on the farm (Carolan 2006;Gardezi and Stock 2021;Jayashankar et al 2018;van der Burg et al 2020;Wiseman and Sanderson 2018). The value of trust is very important, and the adoption of AI within the sector brings further meaning of how trust will be created and reinforced.…”

Section: Resultsmentioning

confidence: 99%

“…For example, some of the papers were in social science journals, but there were also some in engineering and computer science journals (Aggarwal and Singh 2021;Albiero 2019;Krishnan and Swarna 2020;Marinoudi et al 2019;Shamshiri et al 2018). Not surprisingly, the articles by social scientists and ethicists had the greatest emphasis on social and ethical themes, which can be illustrated in Table 2 (Gardezi and Stock 2021;Rose et al 2021;Ryan 2020Ryan , 2019Sparrow and Howard 2020;Stock and Gardezi 2021). This is understandable as much of the focus of these papers is directed towards ethical and social issues, while the papers found in the engineering and computer science journals were more directed towards technical challenges, with ethical issues often having importance, but to a lesser degree.…”

Section: Methodsmentioning

confidence: 98%

“…Ensuring that no harm is done during the deployment of AI is a significant challenge that must be addressed within the field. Some farmers have reported that they believe the use of AI robots and drones may lead to more harmful and riskier farming, particularly towards the natural world, because there is less fear of harms towards humans (Gardezi and Stock 2021). An additional safety concern that was highlighted by Carolan (2020) is farms that 'are highly reliant on automation and robotics will become correspondingly more vulnerable to hacking, sabotage, and corporate espionage' (Carolan 2020, p. 826).…”

Section: Non-maleficencementioning

confidence: 99%

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The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

Ryan

2022

AI & Soc

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This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 98%

Section: Non-maleficencementioning

confidence: 99%

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The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

Ryan

2022

AI & Soc

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“…European Horizon 2020 projects such as FairShare have started to make overview of digital tools and platforms (including social media and the games based tools discussed previously), for both front-office and back-office type activities (Kelly et al 2019, see also https://fairsharepnf.eu/). Some scholars have focused on the economic good nature of digital services and business models (Birner, Daum, and Pray 2021), the sorts of open source and proprietary models and how they may form part of larger services and good packages provided to farmers and the political economy of those (Clapp and Ruder 2020;Daum et al 2021;Gardezi and Stock 2021;Klerkx, Jakku, and Labarthe 2019;Wittman, James, and Mehrabi 2020). Also, there is analysis of how digital tools and advisory service models affect advisory services system management, in view of the real-time data that is continuously gathered (Munthali et al 2018;Namyenya et al, 2021).…”

mentioning

confidence: 99%

“…Other new spaces and dimensions, such as drones which would introduce 'volumetric agriculture' (Klauser and Pauschinger 2021), augment the sensory reach of farmers and advisors but also require new interpretation skills. For farmers and farm workers, processes of deskilling and reskilling and identity change as regards what is 'good farming' have been noted as a (potential) result of digitalization (Brooks 2021;Gardezi and Stock 2021;Ingram and Maye 2020;Klerkx, Jakku, and Labarthe 2019) or perhaps even more pervasive 'de-agrarianization' (Barrett 2021). We also need to understand what this means for advisors and educators.…”

mentioning

confidence: 99%

Digital and virtual spaces as sites of extension and advisory services research: social media, gaming, and digitally integrated and augmented advice

Klerkx

2021

The Journal of Agricultural Education and Extension

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The field of research on agricultural and rural extension and education, also referred to as advisory services and intermediaries, has always engaged with different aspects of the spaces in which agricultural producers (farmers, growers, ranchers) are situated and operate. In this editorial, I will reflect on the elements and environments which jointly constitute and shape the farm (Darnhofer 2020) and their connection with extension and advisory services in the digital age, opening up new digital and virtual spaces. Extension and advisory services receive ample consideration in current debates on digitalization and digital transformation of the agrifood sector, and are an important focus of attention for agriculture and food systems research, practice and policy (Ehlers, Huber, and Finger 2021;Ingram and Maye 2020;Klerkx 2020). In what follows, I will elaborate on some digital and virtual spaces as sites of extension and advisory services research, to progress the field of study for which The Journal of Agricultural Education and Extension serves as a publication outlet.Farms and their environments can be seen as networks of human and non-human actors and entities, also referred to as assemblages (

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Artificial intelligence in farming: Challenges and opportunities for building trust

et al. 2023

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Artificial intelligence (AI) represents technologies with human‐like cognitive abilities to learn, perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm managers to deploy highly targeted and precise farming practices based on site‐specific agroclimatic field measurements. The foundational and applied development of AI has matured considerably over the last 30 years. The time is now right to engage seriously with the ethics and responsible practice of AI for the well‐being of farmers and farm managers. In this paper, we identify and discuss both challenges and opportunities for improving farmers’ trust in those providing AI solutions for PA. We highlight that farmers’ trust can be moderated by how the benefits and risks of AI are perceived, shared, and distributed. We propose four recommendations for improving farmers’ trust. First, AI developers should improve model transparency and explainability. Second, clear responsibility and accountability should be assigned to AI decisions. Third, concerns about the fairness of AI need to be overcome to improve human‐machine partnerships in agriculture. Finally, regulation and voluntary compliance of data ownership, privacy, and security are needed, if AI systems are to become accepted and used by farmers.

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Growing algorithmic governmentality: Interrogating the social construction of trust in precision agriculture

Cited by 40 publications

References 61 publications

The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

Digital and virtual spaces as sites of extension and advisory services research: social media, gaming, and digitally integrated and augmented advice

Artificial intelligence in farming: Challenges and opportunities for building trust

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