Exploring explanation deficits in subclinical mastitis detection with explainable boosting machines

Abstract Subclinical mastitis in cows is a major challenge to the global development of the dairy sector, as it can reduce milk yield and lead to significant financial losses for dairy farmers. Given the difficulty in detecting subclinical mastitis and the growing availability of sensor data during milking, the application of machine learning techniques may offer a solution to more effectively and efficiently detect the problematic condition. Farmers and managers are more likely to use subclinical mastitis prediction tools if they can easily understand how the models work and why they make certain predictions. The advent of Explainable Boosting Machines (EBM) represents an optimal solution, offering results that are both transparent in the decision-making processes and interpretable to domain experts, while maintaining strong predictive power. The study compares models built with readily available data from milking machines to those using more expensive milk characteristic data to determine if there’s an “explanation deficit” when relying on the cheaper, more accessible data. The study finds that while models using the readily available features can perform well in prediction, the explanations derived from them are often less interpretable and harder to align with established veterinary knowledge compared to explanations from models using milk composition data. This suggests a trade-off between data accessibility and the clarity of the model’s reasoning for detecting the condition.