Data Ethics in Agricultural AI: Who Owns the Model That Feeds the World?
Background
The emergence of Artificial Intelligence (AI) in agriculture marks a significant leap towards enhancing food security and sustainability. However, it also raises critical questions about data ethics and ownership. The agricultural sector increasingly relies on AI to optimize processes such as crop monitoring, pest control, and yield forecasting. As AI models become more sophisticated, they require vast amounts of data, often collected from farmers and agricultural enterprises. This raises the question of who owns the data and the resulting models that underpin global food systems. The paper “Recommendations for ethical and responsible use of artificial intelligence in agriculture” provides a comprehensive overview of these challenges, emphasizing the need for ethical guidelines in AI deployment within the agricultural sector (Dara, Hazrati Fard and Kaur, 2022). The authors argue for a balanced approach that considers both technological advancement and the ethical implications of data usage, highlighting the importance of transparency, accountability, and fairness in AI applications. However, the paper notes that more attention is needed on the implications for smallholder farmers, who often lack the resources to influence data governance frameworks—an omission that is critical given their significant role in the global agricultural workforce (Dara, Hazrati Fard and Kaur, 2022).
Challenges and Developments
The primary challenge in agricultural AI is the equitable distribution of benefits and risks. Large agribusinesses often have the resources to leverage AI effectively, while smaller farmers may be left out due to a lack of access to technology and data infrastructure. For example, precision farming tools that utilize AI can significantly increase yield and reduce waste, but they are also expensive and data-intensive, creating barriers for smallholders (Keymakr, 2025). Moreover, the ownership of data generated by AI tools is a contentious issue. Farmers may provide raw data collected from their fields, but once processed by algorithms, the ownership and control of this data often shift to technology providers. This raises ethical concerns about data sovereignty and the potential for exploitation. Additionally, AI models trained on biased data can perpetuate existing inequalities, adversely impacting farmers’ livelihoods. The development of fair AI models necessitates diverse, representative datasets and transparent algorithms, which are currently lacking in many agricultural applications (Dara, Hazrati Fard and Kaur, 2022).
Conclusion
One way to address these challenges is through robust data governance frameworks. Data governance encompasses the policies and procedures that ensure data is managed ethically and transparently. By implementing strong data governance strategies, agricultural stakeholders can clarify data ownership and ensure that farmers are adequately recognized and compensated for their contributions. This also involves setting standards for data sharing and usage, thus preventing exploitation and ensuring that AI models are trained on diverse, unbiased datasets. By fostering an environment of trust and collaboration, data governance can help bridge the gap between large agribusinesses and smallholder farmers, ensuring that AI technologies contribute to a more equitable and sustainable agricultural future (Dara, Hazrati Fard and Kaur, 2022).
References
Dara, R.A., Hazrati Fard, M. and Kaur, N. (2022) ‘Recommendations for ethical and responsible use of artificial intelligence in agriculture’, Frontiers in Artificial Intelligence, 5, 884192. Available at: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.884192/full
Keymakr (2025) Data Bias in AI Agriculture: Ensuring Fairness and Sustainability. Available at: https://keymakr.com/blog/data-bias-in-ai-agriculture-ensuring-fairness-and-sustainability/