AI in Livestock Farming: Ethical Risks and Regulatory Gaps

Artificial intelligence (AI) is revolutionizing livestock farming by enhancing efficiency and automation. However, a new study published in AgriEngineering reveals that this technological advancement also poses significant ethical, environmental, and social challenges. The research, which analyzed 151 peer-reviewed publications from 2015 to 2025, underscores the need for urgent attention to these issues as digital livestock systems expand globally.

The study, titled “Ethics, Animal Welfare, and Artificial Intelligence in Livestock: A Bibliometric Review,” identifies several key ethical risks associated with AI in livestock farming. These include the objectification of animals, algorithmic fairness, data transparency, and labor equity. While AI enables predictive analytics, behavioral monitoring, and automated disease detection, it often reduces animals to data points, neglecting their emotional well-being and behavioral autonomy.

One major concern is the objectification of animals. As AI systems increasingly rely on sensors and computer vision to monitor livestock behavior, animals are often reduced to data points optimized for output. This utilitarian approach tends to neglect the emotional well-being and moral status of animals. The loss of human-animal contact in fully automated systems also raises concerns about alienation and diminished empathy within farming practices.

Algorithmic fairness and transparency are other critical concerns. Many AI systems used in agriculture operate as opaque black boxes, making it difficult for farmers to understand how decisions are made or how errors are propagated. This lack of explainability undermines trust and can perpetuate bias, particularly when data inputs reflect structural inequalities in access to technology and capital.

Data privacy and ownership also present emerging challenges. As AI systems collect vast amounts of behavioral and environmental data, often through cloud-based platforms, questions arise around who controls this information, how it is monetized, and whether small-scale farmers have meaningful oversight or consent.

On the labor front, digitalization threatens to displace rural workers or alter the skill sets required, deepening inequality where digital infrastructure or education is lacking. The study notes that in many low-income regions, limited internet access and training resources exacerbate digital exclusion and reinforce existing socio-economic disparities.

The research identifies the United States, China, the United Kingdom, Brazil, and the Netherlands as the most active contributors to scientific literature on AI ethics in livestock farming. Purdue University, the University of Georgia, Wageningen University and Research, and the University of São Paulo are among the top publishing institutions. Interdisciplinary journals such as Animals, Computers and Electronics in Agriculture, and AgriEngineering dominate the field, indicating a strong convergence of technological and ethical research.

Thematic mapping of the literature reveals four dominant research clusters: AI and precision livestock systems, algorithmic justice and governance, animal welfare and behavioral monitoring, and clinical and veterinary applications. These clusters reflect the interdisciplinary nature of the topic, merging engineering, animal science, digital ethics, and sociology. However, the study finds an imbalance, with most publications favoring technical innovation over normative frameworks, such as transparency requirements or animal rights considerations.

From 2021 onward, publications surged, suggesting a turning point in global concern over the ethical and social implications of smart agriculture. This growth aligns with the broader emergence of Agriculture 4.0 and reflects mounting public interest in food system sustainability and animal welfare in high-tech farming environments.

To address these issues, the study proposes a multi-pronged ethical roadmap. First, it calls for the development of regulatory frameworks and industry-wide codes of conduct. These should include algorithmic auditability, explainability of decisions, fair data access, and participatory oversight mechanisms that involve farmers, developers, and policymakers alike.

Second, it recommends education initiatives to promote digital inclusion and equitable access to smart technologies. This includes funding technical training for rural producers, deploying affordable sensor systems, and ensuring public-sector investment in infrastructure for underserved regions.

Third, the study emphasizes the need for culturally informed ethical approaches. In the Global South, where economic constraints and different moral frameworks shape farming practices, a one-size-fits-all model of AI ethics is neither practical nor just. Ethical standards must reflect local values, governance capacities, and production realities.

Finally, trust must be a central design principle. Transparency in algorithmic processes, fair distribution of benefits, and recognition of animal sentience are vital to building public legitimacy for AI systems in agriculture. The study warns that if these issues are not addressed, digital livestock farming could deepen existing inequalities, erode human-animal relationships, and reduce the credibility of food systems built on AI.