  Mastitis is a billion dollar health problem for the modern dairy industry,
with implications for antibiotic resistance. The use of AI techniques to
identify the early onset of this disease, thus has significant implications for
the sustainability of this agricultural sector. Current approaches to treating
mastitis involve antibiotics and this practice is coming under ever increasing
scrutiny. Using machine learning models to identify cows at risk of developing
mastitis and applying targeted treatment regimes to only those animals promotes
a more sustainable approach. Incorrect predictions from such models, however,
can lead to monetary losses, unnecessary use of antibiotics, and even the
premature death of animals, so it is important to generate compelling
explanations for predictions to build trust with users and to better support
their decision making. In this paper we demonstrate a system developed to
predict mastitis infections in cows and provide explanations of these
predictions using counterfactuals. We demonstrate the system and describe the
engagement with farmers undertaken to build it.
