Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life

Explainability is essential for high-stakes decisions: The paper argues that AI models used in energy and engineering systems must be transparent and understandable, not just accurate. This is particularly important for critical predictions like remaining useful life (RUL), where decisions about maintenance and repairs directly impact system performance and safety.

"Black box" AI creates trust problems: The authors emphasize that while machine learning techniques improve prediction performance, these models need to be "audited, accounted for, and easy to understand" to be useful for real-world decision-making. Without explainability, users cannot confidently rely on AI predictions, even if they are statistically accurate.

Explainable AI enables better maintenance planning: By making AI predictions transparent and interpretable, organizations can make more informed decisions about when to maintain or repair equipment. This leads to improved planning and ultimately better system performance compared to relying on unexplained predictions alone.

Both local and global explanations matter: The paper distinguishes between understanding individual predictions (local explanations) and understanding overall model behavior (global explanations), suggesting that trustworthy AI requires transparency at multiple levels to fully serve users' needs.

Fairness and bias must be addressed alongside accuracy: The authors stress that AI/ML models used in critical systems must be "fair, unbiased, and explainable"—meaning that achieving good predictions is insufficient without also ensuring the model doesn't discriminate or contain hidden biases.