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. (shrink)

Due to natural phenomena like global warming and climate change, agricultural production is increasingly faced with threats that transcend farm boundaries. Management practices at the landscape or community level are often required to adequately respond to these new challenges (e.g., pest migration). Such decision-making at a community or beyond-farm level—i.e., practices that are jointly developed by farmers within a community—can be aided by computing and communications technology. In this study, we employ a translational research process to examine the social and (...) behavioral drivers of adoption of smart and connected farm networks among commodity crop farmers in the United States. We implement focus groups and questionnaires to bring to the fore views on the use of digital technologies in collaborative contexts. We find that participating farmers are concerned with several issues about the potential features of the network (e.g., the ability to ensure data validity while maintaining data privacy) and the nature of their interactions with the various stakeholders involved in the network management. The participatory approach we adopt helps provide insights into the process of developing technologies that are both actionable and trusted by potential end users. (shrink)