Agricultural Big Data Analytics and the Ethics of Power

Ryan, Mark

doi:10.1007/s10806-019-09812-0

Cited by 35 publications

(34 citation statements)

References 20 publications

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“…AI is used in the healthcare sector to predict the onset of diseases (Yan et al 2019), to detect fraud in insurance (Kancevičienė 2019), to predict crime in law enforcement (Asaro 2019), in agriculture to increase crop yields (Ryan 2019b(Ryan , 2020, in cities to reduce congestion (Ryan 2019a;Ryan and Gregory 2019), and in logistics to identify supply-chain management risks (Jiya 2019). Not only is AI being used for raw data processing and prediction, it is also being embodied in physical constructs, such as robots, drones, and self-driving cars.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

In AI We Trust: Ethics, Artificial Intelligence, and Reliability

Ryan

2020

Sci Eng Ethics

Self Cite

261

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One of the main difficulties in assessing artificial intelligence (AI) is the tendency for people to anthropomorphise it. This becomes particularly problematic when we attach human moral activities to AI. For example, the European Commission’s High-level Expert Group on AI (HLEG) have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI (HLEG AI Ethics guidelines for trustworthy AI, 2019, p. 35). Trust is one of the most important and defining activities in human relationships, so proposing that AI should be trusted, is a very serious claim. This paper will show that AI cannot be something that has the capacity to be trusted according to the most prevalent definitions of trust because it does not possess emotive states or can be held responsible for their actions—requirements of the affective and normative accounts of trust. While AI meets all of the requirements of the rational account of trust, it will be shown that this is not actually a type of trust at all, but is instead, a form of reliance. Ultimately, even complex machines such as AI should not be viewed as trustworthy as this undermines the value of interpersonal trust, anthropomorphises AI, and diverts responsibility from those developing and using them.

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

In AI We Trust: Ethics, Artificial Intelligence, and Reliability

Ryan

2020

Sci Eng Ethics

Self Cite

261

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“…Ethic Ryan (2019) The text addresses that the use of Big Data analysis in agriculture can bring undesirable consequences, such as the exercise of power in the agricultural sector. Data integration and storage Sykuta (2016) The article discusses data privacy and security in a period of transition from precision farming to big data.…”

Section: Data Processingmentioning

confidence: 99%

Big data technology applications in agriculture: a systematic literature review

Tamachiro¹,

Oliveira

Gayer

et al. 2022

Exacta

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Federal do Paraná (2010). Pós graduação em Marketing pela FAE (2015). Gestora da atividade de pesquisa no SEBRAE/PR, atua como consultora no desenvolvimento de estudos e pesquisas para instituição com interação com as áreas de pesquisa de outros SEBRAE/UFs, além de parceiros como FIEP e Fecomércio. Desenvolvimento de metodologias de coleta de indicadores de resultado. Monitoramento dos indicadores institucionais de Imagem, Satisfação e NPS. Conhecimento de ferramentas de BI QlikView (Desenvolvimento e análise) e Modelagem de Processos (Bizagi).

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“…Farmers often distrust the agribusinesses who ask for their data, as they suspect they will re-use these data to build other businesses and services which will benefit them but not the farmer. Furthermore, farmers suspect agribusinesses may sell their data, use them for profiling or as preinformation, prior to their investments on the stock market (Ryan 2020).…”

Section: The (Un)willingness To Share Datamentioning

confidence: 99%

Navigating the Twilight Zone : Pathways towards digital transformation of food systems

Wolfert

Ge²,

Burg³

et al. 2021

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Introduction 1.1 The digital transformation of food systems is in a twilight zone 1.2 The need for a paradigm shift to navigate through the twilight zone 1.3 Responsible research and innovation looked through five lenses 2 Five lenses to be used for navigating the twilight zone 2.1 Business models in the data economy 2.1.1 Data economy and economies of digitalisation 2.1.2 Digitalisation and business models: the micro questions 2.1.3 The meta questions: values and currencies 2.1.4 Ecosystem thinking 2.1.5 Cultivating data ecosystems 2.1.6 Implications for navigating the twilight zone: 2.2 Responsible Data Sharing 2.2.1 The (un)willingness to share data 2.2.2 Fair data markets 2.2.3 Autonomy and control 2.2.4 Care for the commons 2.2.5 Trust 2.2.6 Implications for navigating the twilight zone 2.3 Digital Inclusiveness 2.3.1 Digital infrastructure -availability and affordability 2.3.2 Capabilities -awareness, abilities, and agency 2.3.3 Technological design and power 2.3.4 System complexity 2.3.5 Inclusiveness and exclusiveness as a choice?2.3.6 Implications for navigating the twilight zone Navigating the Twilight Zone -pathways towards digital transformation of food systems | 3 2.4 Integrative Artificial Intelligence 2.4.1 The big data gap 2.4.2 Privacy-preserving AI 2.4.3 Knowledge re-use 2.4.4 Transparency and explainability 2.4.5 An "integration first" approach to AI 2.4.6 Implications for navigating the twilight zone 2.5 Cross-Sectoral Integration 2.5.1 Rural development 2.5.2 Food and Health 2.5.3 Circularity and logistics 2.5.4 Water management 2.5.5 Citizen dialogue platforms 2.5.6 Implications for navigating the twilight zone: 3 A multidisciplinary, collaborative, agile approach for navigating the twilight zone 3.1 Requirements for a multidisciplinary, collaborative, agile approach 3.2 A multidisciplinary, collaborative, agile project approach 3.3 Connecting the dots by Digital Innovation Hubs 4 Conclusions and recommendations 4.1 Conclusions 4.2 Recommendations for a responsible and sustainable digital transformation of food systems 4.

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Agricultural Big Data Analytics and the Ethics of Power

Cited by 35 publications

References 20 publications

In AI We Trust: Ethics, Artificial Intelligence, and Reliability

In AI We Trust: Ethics, Artificial Intelligence, and Reliability

Big data technology applications in agriculture: a systematic literature review

Navigating the Twilight Zone : Pathways towards digital transformation of food systems

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