The emergence and the application of AI-based models (AI) and machine learning algorithms (ML) are rescripting work processes in an increasing range of sectors. The dominant narrative promoted by technology developers, such as John Deere, about these technologies is their ability to increase decision-making, productivity, and efficiency of work processes. This type of framing ‘nudges’ a range of actors to adopt these technologies. For example, in agriculture, farmers are adopting new innovations, such as big data and machine learning as a ‘technological fix’ to bolster productivity, increase efficiency, and improve environmental conditions. The adoption of these technologies allows farmers and farm workers to receive precise farming recommendations about where to sow seed, harvest, and apply pesticides. The idea that AI and ML possess the capacity to make precise decisions regarding farming recommendations can distract agricultural stakeholders from the non-neutrality and the risk surrounding the development and deployment of these AI systems. There is no doubt that AI and ML offer several potential benefits for agriculture. However, the deployment of these technologies also raises several social, political, and economic questions that have implications for all stakeholders, including agrarian workers.

Since the first industrial revolution, technological advancements have consistently changed the nature of work. These innovations have significantly impacted the work done by individuals, altering the skills required and how workers feel about their production process. With the emergence of AI and ML, the changes to work are expected to continue. However, the unique features and applications of AI and ML generate new and conflicting implications for agrarian work. Some optimistic accounts suggest that the application of AI and ML will broaden the scope of work performed by farmers, where existing farming tasks will be automated, freeing up time for creative and innovative work. However, more pessimistic accounts predict that AI will degrade and eliminate jobs performed by farmers and farm workers. These conflicting views indicate a lack of clarity regarding the impact of AI and ML on agrarian labor.

What is clear is that pursuing the benefits of AI and ML will also involve risks where these technologies may have unintended consequences. AI and ML are changing the historical fabric of agrarian labor, where farmers are becoming ‘data gatherers’ collecting thorns of information daily from their farming operations, on and off-farms. This indicates that AI and ML are predicated on exploiting farmers’ farm data to generate recommendations. AI and ML require farmers to interpret yield maps, which are generated by the large amount of data aggregated from the sensors and drones using ML algorithms. This implies that workers with advanced skills will be required to work alongside agricultural robots to interpret complex maps generated through PA technologies, which pushes unskilled laborers into economic precarity.

AI technologies and automation can potentially resolve the issue of the shortage of farm labor. This is because AI has the ability to replace the limited and unpredictable supply of migrant, seasonal, and hired workers in agriculture. Although there are social injustices against marginalized laborers, the adoption of AI and ML in agriculture can reduce the need for unskilled and manual labor. This can result in high agricultural productivity and profits for agricultural capitalists and technology development firms. However, the use of AI and ML by farmers could transform the labor force in agriculture, where tasks that are considered 'unskilled' or 'highly skilled' will be transformed. Therefore, AI will not only reduce the skills required to engage in agriculture successfully but also create new roles and shift how farmers or farm workers conduct farm management practices on and off farms.

The use of AI and ML in agriculture is replacing low-skilled jobs typically held by seasonal, migrant, and minority workers. While these technologies can enhance productivity for higher-skilled workers, they may also reinforce existing social, economic, and racial inequalities in the agricultural sector. This is a significant concern in the US, with a long history of systematic racial inequality in agriculture. For example, the institution of chattel slavery for plantation agriculture and the USDA denying black farmers loans for agricultural investments are just a few examples. The Pigford v. Glickman 1999 lawsuit exposed racial discrimination against African American farmers who required support in the form of loans and financial assistance. Therefore, AI technologies may perpetuate historic racial inequality and class relations by replacing migrant and marginalized farmworkers with machines (Ogunyiola 2021).

The integration of technology in society is inseparably entangled with politics; technology design is not free from values; hence, it cannot be considered as intrinsically good, bad, or neutral, as suggested by Melvin Kranzberg’s first law of technology proposed in 1985. With the idea of non-neutrality of technology, it remains vital to maximize the benefits while minimizing the risk associated with AI and ML; it is necessary to have an ongoing and inclusive conversation about the ethical issues raised by AI and its potential in agrarian labor. Several steps can be taken to mitigate the risks of AI and ML without regulatory frameworks. The first step is the inclusion of all stakeholders in the production process. Democratizing AI and ML is critical to building trust and ensuring no stakeholder is left out. Therefore, farmers and farm workers who are most likely to be affected need to be included in the process of AI and ML development. A principal goal to reduce inequality in access to AI and ML algorithms in agriculture should be based on developing and deploying open-source tools. These will make access to these tools affordable to farmers of all classes. Developing ethical guidelines for the development and deployment of AI in agriculture: It is important to develop ethical guidelines for the development and deployment of AI in agriculture. These guidelines should address issues, such as bias, privacy, and surveillance (Posada et al. 2023).

Indeed, AI and ML are powerful tools that have the potential to revolutionize the agricultural sector. However, using these technologies responsibly and ethically is important to ensure that all stakeholders benefit. By investing in education and training, developing policies to support workers displaced by AI, and developing ethical guidelines for the development and deployment of AI in agriculture, we can minimize the risks of AI and maximize its potential benefits (Ogunyiola and Gardezi 2022). The future of agrarian labor in the age of AI and ML is uncertain. However, these technologies will play a major role in shaping the future of the agricultural sector. It is important to start thinking about how we can ensure that AI and ML are used to create a more just and equitable agricultural system. However, the global inequity of capital flow since colonial times means that remittances sent back by migrant workers and the means of life afforded by work for migrant farm laborers are invaluable. Worldwide increased suffering would be a consequence of denying employment to human migrant workers without first ameliorating exploitative asymmetries in transnational capital flow and resources.