Agency in Livestock Farming—A Perspective on Human–Animal–Computer Interactions
Bibliographic record
Abstract
Abstract The adoption of precision livestock farming (PLF) and advanced artificial intelligence enabled computing technologies is radically altering intensive animal agriculture, yet it also raises urgent questions about animals’ autonomy. In this critical review, I explore animal agency—the ability of animals to make choices and shape their environment—and assess whether human–animal–computer interactions (HACI) in human-centric intelligent systems strengthen or weaken that agency. Using animal cognition research, welfare science, and case studies of automated milking, wearable sensors, and AI-driven monitoring, I identify promising strategies for personalized care and natural behavior promotion. Simultaneously, I outline significant risks including over-surveillance, algorithmic control, and diminished empathetic stockmanship associated with increased automation. I argue that meaningful ethical design must take an animal-centered approach, ensuring technologies expand rather than confine behavioral repertoires. Interdisciplinary methods—integrating engineering, ethology, and ethics—are essential for fostering real empowerment. Equally critical is engaging stakeholders who represent diverse agricultural perspectives, including small-scale, organic, and regenerative operations, to guard against exclusionary “one-size-fits-all” solutions. I also underscore the need to address data privacy concerns, farmer skill transitions, and potential biases embedded within AI. Ultimately, I call for transparent dialogues, thorough impact assessments, and adaptive design principles that put animal agency at the core of digital livestock transformation. By balancing higher productivity with deeper respect for animal autonomy, I propose that human-centric intelligent systems can reconcile moral responsibilities toward humane treatment with the practical realities of global food demand. Through this balanced approach, future innovations in livestock management can uphold both ethical imperatives and operational viability, shaping a new paradigm in which animals are recognized as active participants rather than passive inputs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".