This Seminar addresses the need for Predictive Maintenance (PM) solutions that are not only accurate but also trustworthy, meaning transparent. While Machine Learning (ML) has fostered increasingly advanced predictive capabilities, many state-of-the-art models remain opaque, limiting their adoption in safety-critical domains such as Aerospace and Aeronautics. In these contexts, where regulation and human safety are paramount, transparency must be balanced against accuracy gains. To meet this challenge, we put forth a hierarchy of needs for PM applications in high-risk settings, emphasizing interpretability, explainability, and actionability as primary requirements. Overall, this work aims at shrinking the gap between black-box accuracy and human interpretability, contributing to the design of responsible AI systems that are both effective and trustworthy in critical engineering domains.