Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Abstract

Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

Artificial intelligence in farming: Challenges and opportunities for building trust

“…Problems include biased or unrepresentative datasets used to train models; use of biased models to adversely harm stakeholders, especially from vulnerable or underrepresented social groups, for example in crime prediction, policing, transport, insurance pricing models etc. In similar vein, for agriculture, Dara et al (2022) demonstrate bias through an example of an autonomous apple picking application that is built to collect ripe apples but it is trained on data from red-type apples only. This type of model will classify ripe green apples as raw.…”

“…Explainability can be used for improving the credibility of the models and giving agency to farmers. For example, if farmers think that the decision made by an AI tool to assess carbon dioxide or other greenhouse gas (GHG) emissions on their farm is unfair or inaccurate, then they should be able to contest such assessments, with the AI provider, if the models are explainable (Dara et al., 2022).…”

“…However, in contrast to the merits, several disruptive effects of AI‐enabled PA have been noted (Carolan, 2022; Ogunyiola & Gardezi, 2022). These disruptive effects include that many farmers are concerned about who will own and be able to share farm data, how information about their farming practices will be stored, secured, and how the benefits of PA will be shared between farmers and agricultural technology (agritech) corporations (Dara et al., 2022; Jakku et al., 2019). Such challenges are inextricably related to the broader ethical challenges of AI, ranging from poor explainability to lack of model transparency (Dara et al., 2022), exacerbating existing ethical concerns about data ownership and generating distrust in future AI solutions.…”

“…These disruptive effects include that many farmers are concerned about who will own and be able to share farm data, how information about their farming practices will be stored, secured, and how the benefits of PA will be shared between farmers and agricultural technology (agritech) corporations (Dara et al., 2022; Jakku et al., 2019). Such challenges are inextricably related to the broader ethical challenges of AI, ranging from poor explainability to lack of model transparency (Dara et al., 2022), exacerbating existing ethical concerns about data ownership and generating distrust in future AI solutions. Farmers need to be able to trust AI providers that these technologies will achieve their envisioned goals of environmental and economic sustainability in PA (Gardezi & Stock, 2021; Gardezi & Bronson, 2019).…”

FarmTech: Regulating the use of digital technologies in the agricultural sector

“…The provisions and principles do not offer pragmatic solutions to be implemented affecting their efficiency. This is despite their importance, especially from an ethical and technical perspective (Dara et al, 2022). A serious examination through transdisciplinary research of the interplay between the various complex technical, social, economic and governance systems associated with agriculture is needed before the adoption of an adequate regulatory framework (Shepherd et al, 2020).…”