Exploring Explainable and Causal AI for Feature Selection in Industry 5.0

Recent advances in Machine Learning (ML) are revolutionizing industrial processes, particularly in the context of Industry 5.0, which promotes sustainable and intelligent manufacturing practices such as Zero Waste Manufacturing. However, the application of ML in industrial settings is often constrained by limited labeled data and the high dimensionality of sensor inputs, which hinders both model performance and interpretability. This study addresses the challenges of high-dimensional industrial data by evaluating conventional Feature Selection and Feature Extraction methods, such as ANOVA, Mutual Information, and PCA, as well as more advanced techniques based on Explainable AI (XAI) and Causal AI. In addition to benchmarking traditional methods, we experiment with a novel hybrid feature selection approach that combines SHAP-based feature importance analysis with causal discovery techniques. Experimentation was conducted using a real-world dataset from Carpigiani, a leading manufacturer of ice cream production machines. We aim to predict mixture filling levels to prevent critical freezing events during production. Our results demonstrate that integrating XAI and Causal AI enables the development of more interpretable and reliable ML models, thereby enhancing computational efficiency and decision transparency, a key requirement in Industry 5.0 applications.