Agency in Livestock Farming - A Perspective on Human–Animal–Computer Interactions
Bibliographic record
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 examine animal agency—the capacity for animals to make informed choices and exert control over their surroundings—while scrutinizing how human–animal–computer interactions (HACI) in human-centric intelligent systems may either support or undermine this agency. By drawing on research from animal cognition and welfare science, alongside case studies involving automated milking, wearable sensors, and AI-driven monitoring, I highlight promising avenues for personalized care and the encouragement of natural behaviors. At the same time, I reveal the profound risks of over-surveillance, algorithmic control, and the erosion of empathetic stockmanship that can accompany 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".