Cost-Efficiency and Cost-Effectiveness of XAI in Predictive Maintenance

Predictive maintenance aims to reduce operational costs by anticipating and preventing system failures or inefficiencies. While high-performance AI models such as neural networks offer accurate predictions, their lack of transparency limits their usefulness for guiding interventions. Conversely, explainable AI (XAI) models provide insight but often at the expense of accuracy. This paper proposes a framework for cost-based evaluation of interpretable AI models in predictive maintenance, using both classification and regression contexts. We establish criteria to determine when the benefit of interpretability outweighs any reduction in accuracy and show that the utility of XAI is bounded by the relative cost of maintenance versus failure. These findings offer practical tools for assessing the business case for interpretable models in predictive maintenance and related domains. In research, the criteria enable cost-based evaluation and comparison of alternative machine learning methods for regression and classification.