## 4 Transparency and Explainability in AI-Based Predictive Maintenance  
Model interpretability is critical to AI, as it allows us to gain insights into complex models' black-box nature and build trustworthy models. Various techniques have emerged to show how these models make predictions and decisions. Ref. [150] exhibited two concepts titled explainability and interpretability used in the XAI area. If the model's design is understandable to a human being, it is considered interpretable. On the contrary, explainability ties into the idea that explanation is a form of interaction between humans and decision makers. Explainability is regarded as a post hoc tool because this specification covers the techniques used to transform an uninterpretable model into an explainable one. On the other hand, ref. [151] mentioned that if the models are inherently interpretable, they have their own explanations, which align with the model's calculation. This implies that interpretability inherently includes explainability. In the interpretable model, each step of the decision-making process can be traced. Still, there remains a gap in explaining why this specific sequence of steps was chosen during the decision-making process. Explainable models are interpretable by default, but the reverse is not always true [152], indicating that explainability is a subset of interpretability. In explainable AI systems, it can be challenging to understand how the model reached a decision, but it can grasp the underlying reasoning behind it. XAI takes a broader approach, striving to design inherently transparent and interpretable models for humans. The interpretability techniques are classified in different ways. One way is to separate them into two branches, post hoc and ad hoc, concerning the training process [153]. Post hoc techniques use external tools to analyze the trained models. The techniques rely on input perturbations. That is why they can provide unreliable results in cases like adversarial attacks, as in [154]. Moreover, they are not concerned about the model's inner dynamics and the actions to generate different features. Ad hoc techniques modify the model's inner dynamics to facilitate understanding, and the model comes with some explanations during the training itself. The post hoc techniques require feature analysis, model inspection, saliency maps, proxy models, mathematical analysis, physical analysis, and explanation by using text and case to extract the explanations. They are divided into \"model agnostic\" and \"model specific\". Model-agnostic models also use any model covering neural networks. On the other hand, model-specific techniques are developed as specific to the model. Some examples of post hoc techniques are local interpretable model-agnostic explanations (LIME) [155] and SHapley Additive exPlanations (SHAP) [156], class activation map (CAM) [157], layer-wise relevance propagation (LRP) [158], and gradient-weighted class activation mapping (Grad-CAM) [159]. Ad hoc methods include approaches to explaining the model in an explicitly understandable manner, using hand-crafted criteria for the selection of features or incorporating heuristics based on current physics [160; 161]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Accuracy** & **Cost-Effectiveness** & **Scalability** \\ \hline Deep Learning & High & Moderate & High \\ Machine Learning & Moderate & High & Moderate \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AI methods in PdM systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy** & **Cost-Effectiveness** & **Scalability** \\ \hline AI-based & High & Moderate & High \\ Traditional & Moderate & High & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of AI-based PdM systems with traditional methods. Ref. [162] also classified the interpretation techniques as local and global. Local interpretability provides single predictions, whereas global predicting provides interpretation to make new predictions from model features.