Ethics, Animal Welfare, and Artificial Intelligence in Livestock: A Bibliometric Review

Abstract

This study presents a bibliometric review aimed at mapping and analyzing the scientific literature related to the ethical implications of artificial intelligence (AI) in livestock farming, which is a rapidly emerging yet still underexplored field in international research. Based on the Scopus database, 151 documents published between 2015 and 2025 were identified and analyzed using the VOSviewer version 1.6.20 and Biblioshiny for Bibliometrix (RStudio version 2023.12.1) tools. The results show a significant increase in publications from 2021 onwards, reflecting the growing maturity of discussions around the integration of digital technologies in the agricultural sector. Keyword co-occurrence and bibliographic coupling analyses revealed the formation of four main thematic clusters, covering technical applications in precision livestock farming as well as reflections on governance, animal welfare, and algorithmic justice. The most influential authors, high-impact journals, and leading countries in the field were also identified. As a key contribution, this study highlights the lack of robust ethical guidelines and proposes future research directions for the development of regulatory frameworks, codes of conduct, and interdisciplinary approaches. The findings underscore the importance of aligning technological innovation with ethical responsibility and social inclusion in the transition to digital livestock farming.

1. Introduction

Ethics in artificial intelligence (AI) is a topic of great relevance and complexity in the contemporary era. With the increasingly rapid advancement of this technology, there is an urgent need to explore the ethical implications associated with its development and application [1]. Ref. [2] highlights how digital evolution is revolutionizing genetic breeding and the management of crops such as sugarcane, promoting significant gains in efficiency, sustainability, and data-driven decision-making, including developments that are equally applicable to modern livestock systems. Complementarily, ref. [3] proposes a blockchain-enabled cyber-physical system architecture for smart agriculture, which ensures greater traceability, data security, and transparency in interactions among humans, machines, and environments. These studies demonstrate that the digital transformation of agriculture is not merely a technical matter, but one that raises ethical and regulatory implications that must be responsibly addressed to ensure long-term sustainability and social legitimacy.

AI has played a significant role in several areas of life, from virtual assistants in household devices to decision-making systems in critical sectors such as healthcare, finance, climate change, and food production [4].

One of the main concerns related to AI ethics is the transparency and explainability of systems [5]. As AI algorithms become more complex, it is essential to understand how they make decisions in order to build trust in their recommendations and outcomes. A lack of transparency may lead to biased or unfair decisions, potentially resulting in negative societal impacts [6].

In the context of food production, the livestock industry has undergone a series of transformations driven by AI applications. These technological tools offer numerous advantages, such as increased production efficiency, improved herd management, and optimization of animal farming processes [7].

Historically, livestock farming has been a fundamental sector for providing food and essential resources to humanity. With the growing demand for animal-based products, the livestock industry faces the challenge of increasing productivity without compromising animal welfare or environmental balance [8].

The application of AI in livestock farming encompasses a wide range of technologies, from animal health and behavior monitoring systems to predictive analytics and process optimization tools [9]. Large-scale data collection and processing enable more informed and enhanced decision-making, allowing producers to optimize their operations and achieve more effective outcomes [10].

However, the implementation of AI in livestock systems also raises ethical and moral issues that cannot be overlooked. As new technologies are adopted and traditional practices are replaced by automation, concerns emerge regarding their impact on animal welfare, equity among producers and rural communities, and the environmental consequences of increased productivity [11].

Table 1. Summary of research topics and key characteristics in the field of AI ethics in livestock farming.

Research Topic	Main Focus	Common Approach in the Literature
Animal Welfare	Monitoring, stress, emotions	Use of sensors, computer vision, and AI for behavioral assessment
Algorithmic Justice	Bias, transparency, accountability	Concerns about fairness and ethics in decision-making algorithms
Livestock Automation	Efficiency, productivity	Application of AI for management optimization and cost reduction
Social Impacts	Rural labor, digital inequality	Discussion on digital exclusion and changes in labor profiles
Technological Governance	Regulation, applied ethics	Proposals for regulatory frameworks and ethical codes of conduct

brings together the main research topics related to the ethics of artificial intelligence in livestock farming, highlighting for each one its central focus and the common approaches identified in the scientific literature. This overview illustrates the thematic complexity and diversity of the field, which encompasses technological aspects, as well as ethical, social, and regulatory implications.

The topics presented in Table 1 highlight the complexity and multidisciplinary nature of research on artificial intelligence ethics in livestock farming. On one hand, there is a strong emphasis on technological development aimed at automation, monitoring, and improving productive efficiency through intelligent systems. On the other hand, ethical, social, and regulatory concerns emerge, including algorithmic transparency, fairness in decision-making, and the impact of digitalization on rural labor. This combination of technical and normative aspects suggests that the adoption of AI in livestock farming is not merely an operational innovation, but a structural transformation with profound implications for animals, producers, and society.

This study makes a meaningful contribution to the literature by presenting a systematic and focused bibliometric analysis of the ethical implications of artificial intelligence in livestock farming, which is an area still underexplored in an integrated manner. Unlike broader reviews on AI in agriculture, this work emphasizes specific ethical concerns emerging in the context of animal production, such as algorithmic fairness, transparency, animal welfare, and regulatory challenges. By mapping the main thematic clusters and synthesizing dominant research trends, the study provides a structured overview of the intellectual landscape in this domain, supporting future discussions on responsible innovation and ethical governance in digital livestock systems. In this context, the present review aims to comprehensively examine the ethical consequences of using artificial intelligence in livestock farming through bibliometric analysis. The focus lies in understanding how the academic community has addressed this topic and identifying the key ethical concerns raised by researchers and society at large.

2. Artificial Intelligence and the Livestock Industry

Historically, livestock farming was characterized by marked decentralization, operating on a scale that few individuals could coordinate or manage collectively. Until about a decade ago, most producers lacked access to modern technologies such as high-speed internet, smartphones, and low-cost processing capabilities [12]. However, the current landscape of livestock farming has been undergoing rapid transformation. As the world faces growing challenges related to food security, resource efficiency, and animal welfare, technological tools like artificial intelligence (AI) have emerged as promising solutions, driving productivity gains and operational efficiency [13].

Artificial intelligence refers to a form of intelligence capable of performing tasks that were once exclusive to human cognition, such as visual perception, language processing, comprehension, and communication. It encompasses a variety of approaches, methods, and techniques designed to simulate intelligent behavior [14]. These systems consist of programs and algorithms that can be embedded in physical devices such as drones, sensors, vehicles, robots, or agricultural machinery [15].

Disruptive technologies are those that revolutionize and break away from traditional practices. These transformative innovations offer a deeper and clearer understanding of complex systems—such as biological systems—by fostering the integration of technology and science, enriching our understanding and enabling applications that may have a positive impact across various sectors of society [16].

In the livestock sector, there has been a significant increase in research focused on the adoption of cutting-edge digital technologies and their application to animal monitoring and welfare [17]. This includes innovations such as low-contact sensors, digital collars, and proximal sensing technologies [18]. The integration of advanced machine learning techniques, particularly deep learning, has been extensively applied in the development of practical solutions for the sector [19].

These technologies are particularly relevant for assessing physiological changes in animals [20], monitoring behavioral patterns [21], identifying, evaluating, and mitigating stress [22], and the early detection of infectious diseases [12].

Population growth is also placing increasing pressure on governmental policies and public services, particularly in developing countries that face ongoing challenges in food provision [23]. Technological advancement has therefore been increasingly promoted by both governments and private investors in the food industry, as a means to solve pressing problems, enhance productivity, and stimulate economic development in response to demographic expansion [4].

Nevertheless, livestock systems driven by AI applications raise various concerns across economic, environmental, social, and ethical dimensions. These issues must be addressed through coordinated, interdisciplinary collaboration, as this approach is more likely to yield robust, economically viable, and socially desirable solutions for sustainable agricultural development [24].

3. Ethical Implications

Livestock farming is often associated with significant environmental impacts, such as greenhouse gas emissions, deforestation, and excessive use of natural resources. In this context, the integration of intelligent technologies into the livestock production chain emerges as a potentially transformative tool to promote sustainability, reduce environmental damage, and enhance both production efficiency and management processes [25].

Although the application of disruptive technologies in livestock systems offers promising prospects, it is equally essential to comprehensively address the economic, social, environmental, and ethical challenges that accompany these innovations [26].

Among the economic challenges associated with AI implementation are the high costs of specialized labor, equipment, infrastructure, training, and maintenance. Additional concerns include limited access to stable internet connections, cloud-based data storage, and questions around the long-term sustainability of these systems [15].

Recent bibliometric findings indicate that developing countries, such as Brazil, are increasingly engaged in academic discussions on the ethics of artificial intelligence (AI) applied to livestock farming. However, this engagement coexists with deep structural and ethical challenges related to regional disparities in technological infrastructure, professional training, and digital access. In low-income rural areas, limited connectivity, high operational costs, and a lack of technical training hinder the equitable adoption of AI technologies, increasing the risk of digital exclusion and reinforcing pre-existing social inequalities [27,28]. Furthermore, the lack of locally adapted ethical guidelines and inclusive public policies hinders the technically responsible and morally fair implementation of AI in these contexts [8]. Higher education and technical training initiatives have been identified as key strategies to address these gaps, fostering digital inclusion, distributive justice, and greater autonomy among local producers [29]. Therefore, it is essential to align global ethical frameworks with context-sensitive solutions that reflect the sociocultural and economic realities of the Global South, ensuring that digital transitions in agriculture are both efficient and ethically legitimate.

It is important to consider that farmers’ decisions regarding the adoption of AI technologies are often shaped by significant financial pressures. In many countries, livestock producers operate within tight profit margins and face increasing demands for productivity and efficiency. These economic constraints can limit the range of ethical choices available, leading to the prioritization of cost-effective technological solutions over socially or ethically ideal ones. Therefore, the ethical implications of AI in livestock farming must be understood not only in terms of abstract principles, but also in light of the socioeconomic realities that influence human decision-making in agricultural contexts.

Automation and digitalization are also shifting the dynamics of rural labor. As technology assumes a growing role in livestock management, the skillset required of farmers is evolving, which may influence employment opportunities in the sector and increase demand for professionals with technical knowledge [30].

The introduction of disruptive technologies, such as machine learning and the Internet of Things (IoT), also carries significant environmental implications. It is therefore crucial to conduct rigorous impact assessments to ensure that gains in efficiency do not lead to uncontrolled industrial expansion. Moreover, these technologies must be implemented in ways that minimize the environmental footprint of the livestock sector, ensuring responsible and sustainable development [31].

Caution is needed to ensure that AI does not become a tool for unchecked human dominance over nature [32]. In the pursuit of sustainability-oriented technologies—such as waste management systems and renewable energy generation—it is essential to ensure that technological advancements do not exacerbate existing environmental problems [33].

Concerns also arise regarding health and safety in the workplace. Some studies report that AI can improve safety in agricultural operations by reducing workers’ exposure to harmful chemicals and minimizing on-farm accidents [34]. On the other hand, others argue that AI may lead to riskier behaviors, such as increased use of agrochemicals, due to the reduced involvement of humans in the application process [35]. Additionally, there is concern about the loss of farmer autonomy, as companies increasingly impose constraints on on-farm activities, requiring decision-making to be based on proprietary AI systems [36].

The impact of AI on human–animal relationships has also been questioned. While technology may improve production efficiency, it can lead to a reduction in direct human–animal contact, potentially affecting animals’ emotional well-being and distancing farmers from their livestock [37]. The design of artificial intelligence technologies in livestock farming plays a central role in shaping how animals are perceived and treated. When guided exclusively by a utilitarian logic, these technologies tend to prioritize efficiency, productivity, and control, reducing animals to mere resources to be optimized. However, as argued by [38] this approach overlooks essential aspects of animal life, such as their capacity to feel, interact, and form bonds. The authors emphasize that tactile and affective forms of interaction—like touch—are vital to acknowledging animals as moral subjects. Therefore, technical development should be driven not only by performance goals but also by ethical principles that safeguard animals’ intrinsic interests, including emotional well-being, behavioral autonomy, and meaningful social relationships. Redirecting AI design in this way is crucial to avoid reinforcing the objectification of animals and to promote a truly ethical approach to digital livestock farming.

It is important to note that equal access to AI technologies does not guarantee equity or fairness in outcomes. Even when farmers are able to optimize their operations efficiently, widespread AI adoption in agriculture may lead to capital concentration and deepen existing inequalities [4]. As traditional suppliers of inputs and equipment transition into data-driven agribusinesses, the accumulation of data can be leveraged to generate profit, influence markets, shape innovation agendas, and steer policy and governance structures [39].

The ethical implementation of artificial intelligence in livestock farming requires special attention to the norms surrounding the use of animal-generated data. Recent studies highlight that while the collection and analysis of such data can promote welfare and sustainability, they also raise concerns about commodification and the reduction of animals to mere data points [8]. Additionally, ethical and social risks related to animal data privacy and governance arise in the absence of clear guidelines for responsible use [8]. Therefore, we recommend the development of specific guidelines to define how this data should be collected, stored, and utilized transparently and with appropriate consent, while respecting principles of animal welfare and social justice. The literature also suggests that multi-stakeholder initiatives are essential to establish governance structures that are context-sensitive and adaptable to regional, cultural, and economic particularities in livestock production [40].

Given the significant growth in research on AI ethics in livestock farming, it is crucial to understand how the academic community is addressing these issues and where the key gaps in the literature lie. This bibliometric review sought to map the global landscape of studies on AI ethics applied to livestock systems, identifying emerging research trends, current challenges, and opportunities for regulation and innovation. To this end, we employed scientific performance indicators and network mapping techniques—including co-authorship and keyword co-occurrence analysis—to understand which topics are receiving attention and where further investigation is needed.

4. Bibliometric Analysis of Articles on Ethics and Artificial Intelligence in Livestock Farming over the Last Decade (2015–2025)

This study adopted a combined approach of literature review and bibliometric analysis to explore the global landscape of scientific publications addressing the ethical implications of artificial intelligence (AI) in livestock farming. Data collection was conducted using the Scopus platform (www.scopus.com), one of the largest multidisciplinary databases of scientific abstracts and citations (accessed on 12 February 2024).

The search strategy employed the following terms: “Artificial intelligence” AND “digital livestock” AND “ethics” AND “animal welfare”, applied to the title, abstract, and keyword fields of indexed documents. Only documents classified as “Articles” and “Reviews”, written in English, and published between 2015 and 2025 were considered. A total of 151 documents were selected for analysis.

From this publication set, relevant bibliometric indicators were extracted, including the total number of publications, the most cited articles, the most productive authors, the institutions with the highest scientific output, the most active countries, and the most frequently used keywords.

To assess the scientific performance of authors, journals, and countries, thematic mapping and network analysis techniques were applied using VOSviewer and Biblioshiny (RStudio). These tools enabled the visualization of co-authorship networks, keyword co-occurrence patterns, and bibliographic coupling, offering a graphical representation of the relational dynamics within the field. The application of these techniques allowed the identification of key thematic clusters and the underlying intellectual structure of the research domain.

Figure 1 displays the flowchart of the bibliographic screening process adopted in this study, based on standardized selection criteria from the Scopus database.

The bibliographic screening process was conducted in a structured and replicable manner, following standardized inclusion criteria aligned with best practices in bibliometric reviews. This ensured a consistent and reasonable selection of documents for analysis.

4.1. Performance Analysis

Performance analysis is a defining characteristic of bibliometric studies that focus on the contributions of research elements related to the ethical use of artificial intelligence (AI). This descriptive approach aims to evaluate the performance of authors, countries, and journals by using bibliometric data collected from the Scopus academic database, covering the period from 2015 to March 2025.

Figure 2 illustrates the annual growth of publications addressing the ethical implications of AI use in livestock farming, showing the number of publications per year.

The annual evolution of publications on the ethical use of artificial intelligence (AI) in livestock farming, in high-impact international journals, reveals a significant increase in scientific output over the past decade (Figure 2). Between 2015 and 2020, the number of published studies remained low and relatively stable, with no more than four publications per year, suggesting that interest in the topic was still emerging. From 2021 onwards, a sharp rise is observed, peaking at 51 publications in 2024, which may reflect the maturation of ethical discussions surrounding AI adoption in the livestock sector, as driven by technological advancements, growing concerns about animal welfare, and increasing sustainability pressures. The slight decrease in 2025 (15 publications recorded at the time of data collection) is attributed to the partial nature of the data, which covers only up to March of that year. Overall, the trend confirms a growing recognition of the ethical relevance of AI in contemporary livestock production.

This increase in publications correlates with a global concern about the integration of digital technologies into animal farming practices, including issues such as animal objectification [41], reduced human–animal interaction [8], algorithmic bias [42], displacement of human labor [43], and sustainability [34].

With the rapid advancement and growing application of artificial intelligence (AI) in agriculture, complex ethical debates have emerged, involving aspects such as programming systems to make moral decisions, ensuring transparency in decision-making processes so they remain understandable to humans, and defining accountability for actions taken by these autonomous systems [44].

Figure 3 illustrates the fifteen countries with the highest scientific contribution to the debate on the ethical implications of AI, highlighting a network of international collaboration, with key hubs in North America, Europe, and Asia, and a modest but significant presence in South America, represented by Brazil.

The United States (38 publications), China (22), the United Kingdom (14), Brazil (12), and the Netherlands (10) stand out as the most prominent and influential countries in scientific production on this topic, as shown in Figure 2. These countries recorded a significant number of citations—ranging from 540 to 965—between 2015 and March 2025, reflecting not only high publication volume but also strong impact and relevance in the field. The international co-authorship network suggests a high degree of interconnectivity among these nations, indicating active collaboration and global knowledge exchange within this area of research. The size of each node represents the number of publications, while the connecting lines indicate co-authorship links, reflecting the intensity of international collaboration.

Figure 4 presents the ten most active and influential research institutions and affiliations in terms of bibliographic contributions to the subject.

Figure 4 presents the institutions with the highest number of publications related to the ethical use of artificial intelligence in livestock farming, highlighting the concentration of scientific knowledge within leading academic centers. Purdue University ranks first with seven articles, followed by the University of Georgia and Wageningen University and Research, each with six publications. Notable contributions also come from institutions such as Iowa State University and the University of São Paulo (USP), both with five articles, underscoring the significant role of Latin America. Other influential universities include China Agricultural University, Cornell University, Dalhousie University, Freie Universität Berlin, and Louisiana State University, each contributing four publications. These data reveal a scientific production axis distributed across North America, Europe, Asia, and South America, reflecting growing global attention to the ethical implications of AI adoption in the livestock sector.

The analysis of the core characteristics of relevant journals provides guidance for identifying the pertinent literature and acquiring foundational knowledge in the research area, while also reflecting the impact and significance of published work [45]. A total of 151 articles on AI ethics in livestock farming were published across 84 journals, with the ten leading journals—based on the number of published documents—presented in Figure 5.

Figure 5 presents the journals with the highest number of publications related to artificial intelligence ethics in livestock farming between 2015 and 2025. The journal Animals leads by a wide margin, with a total of 23 publications, reflecting its central role in disseminating interdisciplinary studies that address animal welfare, technology, and ethics. It is followed by Computers and Electronics in Agriculture (11 articles) and the Journal of Library and Information Science in Agriculture (5 articles), indicating the involvement of journals specialized in both agricultural technology and information management. Other relevant journals include AgriEngineering, Agriculture (Switzerland), and Applied Animal Behaviour Science, which together demonstrate the thematic and disciplinary diversity of scientific output on the subject. These findings highlight that the ethical application of AI in livestock farming has been absorbed by journals from various fields, reflecting the cross-cutting nature and growing academic importance of the topic.

Overall, the performance analysis reveals an increasingly dynamic and consistent trend in research dedicated to the ethical dimensions of artificial intelligence, especially from 2021 onward. This advancement is reflected both in the rising number of publications from prominent institutions and in the concentration of studies in relevant scientific journals. Such a movement signals a growing level of academic concern with the social, moral, and regulatory implications of AI adoption, particularly in sensitive sectors such as livestock farming.

4.2. Scientific Mapping

Scientific mapping is based on textual information extracted from titles and abstracts, using keyword analysis to highlight prevailing research trends and examine the connections among previously identified research elements [46]. Citation analysis is also employed to expose relationships between publications and identify those with greater scholarly influence. Additionally, co-authorship analysis is applied to identify collaboration patterns among contributing authors [47].

Bibliographic coupling is a scientific mapping approach that relies on the assumption that two documents sharing common references are also likely to share similarities in content [48].

Figure 6 displays a keyword co-occurrence network map generated from the 151 analyzed documents, allowing for visualization of the thematic structure of the literature on ethics and artificial intelligence (AI) in livestock farming. Each node represents a keyword, with its size proportional to the frequency of occurrence, while edges (links) indicate co-occurrence relationships within the same articles, suggesting conceptual proximity. Only keywords with a minimum of six occurrences were considered, resulting in 49 terms grouped into four thematic clusters.

The network analysis revealed a central cluster (red), which concentrates the main structuring concepts of the field, such as “artificial intelligence”, “precision livestock”, “machine learning”, and “AI ethics”. This group reflects the dominant focus in the literature: the application of intelligent technologies in precision livestock farming, with growing attention to the ethical implications involved. The green cluster includes terms such as “nonhuman”, “deep learning”, and “artificial neural network”, emphasizing a methodological orientation toward computational modeling and experimentation with non-human technical applications. The blue and yellow clusters focus on more operational dimensions: the blue cluster relates to animal monitoring and visual automation, including terms like “livestock” and “computer vision”, while the yellow cluster centers on the interface between AI and veterinary practices, covering topics such as “diagnosis”, “physiology”, and “cattle disease”.

These groupings highlight the interdisciplinary nature and thematic complexity of the field, which combines technological innovation with ethical, productive, and clinical concerns. This configuration reveals an expanding research network that articulates multiple dimensions of digital transformation in the livestock sector.

Nevertheless, the literature also warns of the risks and dilemmas associated with the rapid integration of AI into livestock systems. Cluster analysis reflects emerging ethical concerns that accompany this technological advancement. As noted by [49], six core categories of ethical risk have been identified: fairness, transparency, accountability, sustainability, privacy, and robustness. The authors recommend that technology providers and policymakers take proactive steps to mitigate threats such as data privacy violations, lack of accountability, and impacts on animal welfare.

This perspective aligns with [8], which highlights the risk of objectifying animals as mere data points and the reduction of human–animal interaction in highly automated environments. Complementarily, ref. [50] argue that trust is a central element for the social acceptance of AI in agriculture, emphasizing that the absence of clear governance undermines the legitimacy of such technologies.

This analysis also resonates with studies such as [51], which propose a global convergence around five fundamental ethical principles for AI: transparency, fairness, non-maleficence, responsibility, and privacy. The presence of these underlying values within the bibliometric and thematic discussions examined in this study reinforces the urgency of developing regulatory frameworks, codes of conduct, and participatory governance models to ensure an ethical, fair, and sustainable transition to digital livestock farming.

Bibliographic coupling is a literature analysis technique that evaluates similarity between documents based on the number of shared references. The greater the number of sources cited in common, the higher the intellectual proximity between the documents, indicating thematic affinity and conceptual convergence [52].

Based on the keyword co-occurrence analysis performed using VOSviewer, four major thematic clusters were identified, each representing a distinct research focus within the field. These categories include: (1) AI technologies and precision livestock farming, (2) algorithmic justice and ethical governance, (3) animal welfare and behavioral monitoring, and (4) clinical and veterinary applications.

Table 2. Thematic clusters generated from keyword co-occurrence and their characteristics.

Thematic Cluster	Dominant Keywords	Thematic Description
1. AI Technologies and Precision Livestock Farming	artificial intelligence, machine learning, precision livestock, smart farming	Refers to the application of intelligent technologies for monitoring and automating production processes. Emphasizes the use of AI for decision-making, productivity gains, and operational efficiency.
2. Algorithmic Justice and Ethical Governance	AI ethics, fairness, transparency, accountability	Groups publications focused on the ethical risks related to the use of algorithms in livestock farming, such as bias, lack of transparency, accountability for errors, and the need for regulatory guidelines.
3. Animal Welfare and Behavioral Monitoring	animal welfare, livestock, computer vision, stress detection	This cluster focuses on the use of AI to assess animal welfare, highlighting computer vision techniques and sensors to monitor behavior, health, and emotional states.
4. Clinical Applications and Veterinary Practices	diagnosis, physiology, cattle disease, veterinary AI	Encompasses studies on AI applications in veterinary diagnostics, animal physiology, and disease prevention, reinforcing the clinical role of technology in livestock production.

summarizes the dominant keywords and thematic descriptions of each cluster. This classification provides a clearer understanding of the conceptual structure of the literature and highlights the main areas of academic interest in the ethical implications of AI in livestock systems.

Figure 7 illustrates the bibliographic coupling network among the main authors contributing to the literature on ethics and artificial intelligence in livestock farming. This analysis allows for the identification of significant connections between works that share common references, revealing thematic closeness and intellectual influence. Notably, author [44] appears as a central figure in the network, with the highest number of citations and connections, underscoring their foundational importance to the field.

The study by [44] offers an exploratory review of the social science literature on digital agriculture, Agriculture 4.0, and smart livestock farming. The authors identify five thematic clusters—ranging from technology adoption in the field to ethical, economic, and institutional issues—and propose a future research agenda focused on ethical governance and transdisciplinary development in the digital agri-food sector. Its substantial citation count (795) reinforces its value as a well-established conceptual reference.

Complementarily, ref. [53], cited 73 times, address advances in sensors and intelligent decision-support tools applied to precision livestock farming, highlighting the importance of integrating mechanistic models with artificial intelligence to develop more efficient and sustainable hybrid systems. This integration aims not only to improve productivity but also to ensure more ethical and responsible resource management and animal welfare.

While AI applications in livestock systems are promising, they also raise significant ethical challenges. Ref. [54] draws attention to risks such as algorithmic bias, opacity in automated decision-making processes, and the ethical implications of interactions between humans, animals, and intelligent technologies. These issues become even more critical when considering the entire supply chain—from animal management to final processing—calling for transparency, equity, and accountability as the pillars of technological governance.

The literature also reveals growing interest in the cognitive and social dimensions of interactions with intelligent systems. The work of [55] proposes an interdisciplinary approach to human–robot interaction by integrating neuroscience, psychology, and AI to understand how intelligent machines are incorporated into human social environments. With 77 citations, the article stands out for addressing the ethical and emotional aspects of such interactions—topics increasingly relevant in the context of digital livestock farming, as the use of robotics may weaken the bond between producers and animals.

In this regard, ref. [8] warns of the risks of animal objectification, the digital divide among producers, and the loss of the human–animal connection. Such risks demand robust ethical safeguards, including codes of conduct, regulatory guidelines, and participatory governance models. This concern is echoed by [56], who emphasize that digitalization should not compromise animal welfare or dehumanize care practices.

Finally, authors such as [57] argue that beyond technical innovation, it is essential to ensure equitable access to technology and promote a digital transformation guided by ethical and social values. This convergence of production, animal welfare, and ethics reinforces the need for a more inclusive and comprehensive regulatory ecosystem capable of aligning technological innovation with social justice and sustainability.

In this context, robust and effective governance structures are fundamental to ensuring that the development and implementation of AI occur in an ethical, safe, and inclusive manner. These structures serve as mediators among different stakeholders—including producers, technology companies, researchers, consumers, and policymakers—promoting trust, system interoperability, and value alignment. By establishing clear guidelines for ethical AI use and creating oversight and accountability mechanisms, such frameworks contribute to risk mitigation, enhance the benefits of digitalization, and strengthen the social legitimacy of technological innovations in the livestock sector.

Building ethical and effective governance for AI in livestock farming requires more than adherence to normative principles. It demands the strengthening of trust-based relationships among stakeholders across the agri-food sector, including farmers, researchers, technology developers, and policy makers. The application of AI in this context involves relevant ethical risks, such as the collection and use of sensitive data, the transparency of automated decision-making processes, and the potential amplification of pre-existing structural inequalities. In this scenario, ref. [50] emphasizes that although many researchers acknowledge their ethical responsibility in AI development, they still face structural barriers such as the lack of clear regulatory guidelines and institutional fragmentation. For these authors, trust—understood as a continuous relational process—is a central component in ensuring the social acceptance and legitimacy of AI in agriculture. Thus, initiatives grounded in transparency, inclusion of diverse perspectives, and shared accountability are essential to building a truly ethical, fair, and socially sustainable technological ecosystem in the agri-food sector.

Beyond the bibliometric patterns identified, there is clear evidence of an exponential growth in scientific interest in the ethical use of AI in livestock farming from 2021 onwards. This temporal shift coincides with the global maturation of discussions around the governance of disruptive technologies in sensitive domains such as animal health, food security, and welfare. The concentration of the publications in interdisciplinary journals and the geographical diversity of the authors highlight the cross-cutting nature of the topic and its growing presence in international research agendas.

However, the findings also reveal important conceptual and practical gaps. There is a predominance of technical and applied approaches, with fewer studies addressing normative frameworks or offering in-depth analysis on algorithmic justice, transparency, animal autonomy, and the implications of objectifying living beings in digital contexts. This asymmetry points to the need to strengthen dialogue among fields such as animal science, applied ethics, digital law, and public policy.

The increasing adoption of artificial intelligence (AI) technologies in digital livestock farming raises important concerns about algorithmic transparency and ethical accountability. The literature identifies three key operational criteria for assessing transparency: auditability, interpretability, and explainability. Auditability refers to the ability to review algorithmic processes to identify failures or biases; interpretability concerns how clearly humans can understand the model’s logic; and explainability pertains to the system’s capacity to justify its decisions in a way that is accessible to end users [58,59].

In addition, it is essential to develop accountability mechanisms to address errors arising from data bias, which can emerge at any stage of the AI lifecycle, from producers to technology suppliers and regulators. Recent studies recommend regular algorithmic audits, inclusive data practices, and clearly defined responsibilities for each actor involved in decision-making [60,61].

As a theoretical and practical pathway, the principle of distributed responsibility has gained traction, recognizing that multiple stakeholders share ethical duties in complex sociotechnical systems such as digital livestock farming. This approach supports collaborative governance and facilitates the development of more robust policies to ensure fair and transparent algorithmic decision-making [62,63].

In terms of originality, this review stands out for offering a systematic and quantitative mapping of how ethics and artificial intelligence (AI) have emerged specifically within the domain of livestock farming, which is a perspective still underexplored in the international literature. By integrating bibliometric analysis with critical interpretation and practical recommendations, the study helps bridge a meaningful gap between dispersed scientific output and the development of a more structured ethical agenda. This contribution becomes even more significant given the urgent need to align technological innovation with ethical and sustainable principles, under the risk of undermining the social and environmental legitimacy of digital agriculture.

Accordingly, this study also highlights the lack of well-defined ethical and regulatory guidelines—particularly in developing countries—and suggests that future research should advance the creation of standards, codes of conduct, and accountability mechanisms to guide ethical AI use by developers, producers, and policymakers. It is hoped that this work will inspire interdisciplinary research and contribute to the development of public policies informed not only by data, but also by values.

5. Conclusions

This study conducted a comprehensive bibliometric review on the ethical dimensions of artificial intelligence (AI) in livestock farming, aiming to map the scientific output, identify thematic trends, and highlight key gaps and challenges associated with the digital transformation of the agricultural sector. The analysis of 151 documents published between 2015 and 2025 revealed a significant increase in academic interest in the topic, especially from 2021 onward, reflecting the growing maturity of ethical, social, and technological discussions surrounding the adoption of AI in precision livestock systems.

The findings indicate that, despite notable progress in technological applications and intelligent herd monitoring, important gaps remain in the debate around accountability, algorithmic transparency, animal welfare, and digital inclusion. Moreover, there is an urgent need to integrate interdisciplinary perspectives that take into account the ethical, regulatory, and cultural dimensions involved in implementing these technologies.

The main contribution of this study lies in exposing the current conceptual fragmentation of the literature and proposing a critical agenda aimed at building ethical governance frameworks, developing codes of conduct, and formulating public policies that ensure a responsible, fair, and sustainable technological transition in the livestock sector. It is hoped that the findings presented here will support future interdisciplinary research and encourage the strengthening of regulatory frameworks and ethical practices at the intersection of AI and animal production.

6. Future Perspectives

The results of this bibliometric review indicate that the discussion on ethics and artificial intelligence (AI) in livestock farming is still in an emerging stage, with recent growth and thematic concentrations that remain fragmented. In this context, several priority directions for future research are identified:

• Expansion of interdisciplinary approaches: integrating agricultural sciences, social sciences, applied ethics, computer science, and public policy is essential to comprehensively understand the impacts of AI in livestock systems and its social, environmental, and economic implications.
• In addition to bibliometric mapping, future studies should incorporate qualitative methods that explore ethical dilemmas and sociocultural perceptions in depth. Empirical case studies that combine agricultural sciences, moral philosophy, and rural sociology can help identify value conflicts and practical challenges faced by producers, technicians, and policymakers in the adoption of AI technologies. These interdisciplinary approaches are essential for developing ethically grounded, context-sensitive guidelines that reflect real-world experiences and support responsible innovation in digital livestock farming.
• Development of ethical and regulatory guidelines: there is an urgent need for research focused on building codes of conduct, responsible use protocols, and governance models that guide the ethical application of AI in agriculture, particularly in contexts with weaker regulatory frameworks.
• Empirical studies on social and cultural impacts: the literature lacks field studies that evaluate how AI-based technologies affect relationships among farmers, animals, and machines in different socioeconomic and cultural settings, especially in the Global South.
• Exploration of trust as a central element: future investigations should deepen the analysis of trust in the adoption of intelligent systems, taking into account factors such as algorithmic transparency, technological accessibility, and user participation in decision-making processes.
• Digital inclusion and technological equity: research on strategies to mitigate the digital divide among farmers is critical to ensuring that advancements in digital livestock farming do not reinforce existing structural inequalities in rural areas.
• Ongoing ethical monitoring of innovations: it is recommended to develop continuous ethical assessment mechanisms that monitor the life cycle of AI technologies—from design to deployment—focusing on animal welfare, data privacy, and fairness.