By emphasising cattle social behaviour as a key welfare indicator, the paper outlines design principles to enhance the interpretability, relevance, and trustworthiness of artificial intelligence systems.
Abstract
Monitoring cattle social behaviour is fundamental for assessing animal welfare in modern penned production systems. Traditional observation methods are constrained by subjectivity, labour demands, and limited scalability, prompting increased interest in artificial intelligence (AI) for automated behaviour tracking. While recent advances in computer vision, sensor technologies, and machine learning offer promising tools for continuous and objective monitoring, many systems focus on identifying “what” an animal is doing (e.g., lying, feeding), without interpreting the underlying “why”, such as whether a posture indicates rest, discomfort, or illness, due to lack of contextual modelling.