ArticleFrontiers in veterinary science2025
AI for One Welfare: the role of animal welfare scientists in developing valid and ethical AI-based welfare assessment tools.
Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integrating agentic artificial intelligence into pasture-based dairy systems: Applications, governance, and future directions.JDS communications · 2026Article
- Overweight and Obesity in Dogs and Cats: An Exploration of Animal Welfare and Behaviour Impacts, and Recommendations for Management in Veterinary Primary Care.Animals : an open access journal from MDPI · 2026Review
- Animals as Communication Partners: Ethics and Challenges in Interspecies Language Research.Animals : an open access journal from MDPI · 2026Review
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5 authors.
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Abstract
The increasing use of artificial intelligence (AI) in livestock farming is accelerating the development of automated welfare assessment tools, particularly with advancement in generative AI such as large multimodal models (LMMs). Yet, animal welfare scientists have rarely been involved in the development process of these tools or their subsequent adaptation within the field. Here, we discuss possible roles for animal welfare scientists in the development and validation of AI-based welfare assessment tools. We first examine key uncertainties that emerge during development, including the selection of relevant, valid and reliable welfare indicators and gold standards, hardware and software solutions for data collection, methods for integrating multiple welfare indicators, and the real-world impact of automated welfare assessment tools. Second, we demonstrate the use of LMMs to assess welfare based on a case study using dairy cow cleanliness. Finally, we consider the practical implementation of AI-based welfare assessment and discuss potential tensions around (1) embedded values in LMMs, (2) AI's influence on decision-making on farms, (3) the integration of AI in current knowledge systems by human-AI collaboration, and (4) the economics of AI-based welfare assessment and improvement. We conclude that LMMs could help automate welfare assessment and communicate results to humans in accessible formats, but outcomes depend on which stakeholders are involved in the development process. We advocate for developing AI-based welfare assessment tools through the One Welfare framework, recognizing that AI deployment affects humans, animals, and the environment simultaneously, and suggest potential pathways for animal welfare scientists to engage in the process.
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