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AbstractThis 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.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.

AbstractDigital farming technologies promise to help farmers make well-informed decisions that improve the quality and quantity of their production, with less labour and less impact on the environment. This future, however, can only become a reality if farmers are willing to share their data with agribusinesses that develop digital technologies. To foster trust in data sharing, in Europe the EU Code of Conduct for agricultural data sharing by contractual agreement was launched in 2018 which encourages transparency about data use. This article looks at the EU Code through the lens of literature on trust and contract agreements. We agree with the makers of the EU Code that a contract can make an important contribution to trust relationships as it is needed to mitigate the detrimental effects of power relationships between experts and non-experts. Building on Onora O’Neills perspective to trust, however, we argued that a contract can only be successful in fostering trust when (a) information is comprehended by the more vulnerable party in this relationship who has to sign the contract, (b) the more powerful partner takes responsibility to provide that information, and (c) information is tailored to the information needs of the party signing the contract, even when data are re-used over a longer period. In addition, we think that differences between trust relationships and relationships of accountability, give reason to add to informed consent other more substantive ethical components in a more encompassing code of conduct.

Smart farming (also referred to as digital farming, digital agriculture and precision agriculture) has largely been driven by productivity and efficiency aims, but there is an increasing awareness of potential socio-ethical challenges. The responsible research and innovation (RRI) approach aims to address such challenges but has had limited application in smart farming contexts. Using smart dairying research and development (R&D) in New Zealand (NZ) as a case study, we examine the extent to which principles of RRI have been applied in NZ smart dairying development and assess the broader lessons for RRI application in smart farming. We draw on insights from: a review of research on dairy technology use in NZ; interviews with smart dairying stakeholders; and the application of an analytical framework based on RRI dimensions. We conclude that smart dairying R&D and innovation activities have focused on technology development and on-farm use without considering socio-ethical implications and have excluded certain actors such as citizens and consumers. This indicates that readiness to enact RRI in this context is not yet optimal, and future RRI efforts require leadership by government or dairy sector organisations to fully embed RRI principles in the guidelines for large R&D project design (what has also been referred to as ‘RRI maturity’). More broadly, enacting RRI in smart farming requires initial identification of RRI readiness in a given sector or country and devising a roadmap and coherent project portfolio to support capacity building for enacting RRI. Additionally, methods (such as RRI indicators) for operationalising RRI must be adapted to the context of the national or sectoral innovation system in which smart farming is being developed.