PERSPECTIVE article
Front. Vet. Sci.
Sec. Animal Behavior and Welfare
Volume 12 - 2025 | doi: 10.3389/fvets.2025.1645901
This article is part of the Research TopicThe Future of Farm Animal Welfare Science: Selected Papers from the 9th International Conference on the Welfare Assessment of Animals at Farm Level (WAFL)View all 7 articles
AI for One Welfare: the role of animal welfare scientists in developing valid and ethical AI-based welfare assessment tools
Provisionally accepted
- 1Centre for Animal Nutrition and Welfare, Clinical Department of Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria
- 2Animal Welfare Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, Canada
- 3Messerli Research Institute, University of Veterinary Medicine Vienna, Vienna, Austria
- 4Precision Livestock Farming Hub, Clinical Department of Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria
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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.
Keywords: Generative AI, large multimodal model, AI alignment, Welfare quality, animal interests, precision livestock farming
Received: 12 Jun 2025; Accepted: 18 Jul 2025.
Copyright: © 2025 Foris, Sheng, Dürnberger, Oczak and Rault. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Borbala Foris, Centre for Animal Nutrition and Welfare, Clinical Department of Farm Animals and Food System Science, University of Veterinary Medicine Vienna, Vienna, Austria
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