## 5 Analysing General XAI  
The concept of XAI is comparatively contemporary to other concepts of AI and the term \"explainability\" is deliberately used in various research domains including AI. Thus, it is expedient to develop a general understanding of the term explainability from the perspective of XAI. The prime hindrance towards developing the ground knowledge of \begin{table}
\begin{tabular}{l l l l} \hline \hline
**AI/ML Models** & **Prediction** & **Optimisation/Automation** & **Analysis** & **Modelling/Simulation** \\ \hline Recurrent Neural Network & & & \\ (RNN) \& Long Short Term & [43; 152; 156; 184; 185; 243; 251] & [186] & [252] \\ Memory (LSTM) & & & \\ \hline Multi-Layer & & & \\ Preceptron (MLP) & [125; 163; 251] & & \\ \hline Graph-Theoretic & & & \\ Clustering & & & \\ \hline Hidden Markov & & & \\ Model (HMM) & & & \\ \hline Markov Decision & & & \\ Process (MDP) & & [78; 235; 240; 255] & & \\ \hline Not referenced & [256; 257] & [258] & [259] & [260] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._explainability concerning AI, is the interchangeable use of several terms in the literature, such as: Interpretability, transparency, explainability, etc. Before proceeding to the literature review, the commonly used terms are presented briefly according to the definitions compiled by [PERSON] et al. [261]. \"Understandability\", often termed as \"Intelligibility\", is the characteristics of a model that helps a user realise its functions. In other words, how the model works without any requirement of further explanation for the model's internal operations on the data. Another similar term is \"Comprehensibility\", which has been used to define the ability of an ML model to represent its learned knowledge to humans in an understandable way. Clearly the prior terms differ from the second on representing the internal operations on the data and the knowledge acquired from the data. In addition, the terms \"Interpretability\" and \"Transparency\" are mostly used in describing similar concepts to explainability and they refer to a model's ability to provide meaning or explain in an understandable way to human beings. Nonetheless, model transparency also indicates the ability to be understandable to humans. There are three types of transparent models [262]:  
* Simulatable models have the capacity to make humans understand their structure and functioning entirely. * Decomposable models can be decomposed into individual components, i.e., input, parameters and output, and their respective intuitions. * Algorithmically Transparent models behave \"sensibly\" in general with some degree of confidence. Above all, the term \"Explainability\" affiliates the interface between humans and decision-makers, which is concurrently comprehensible to humans and an accurate representation of the decision-maker [263]. In XAI, explainability is the interface between the models and the end-users through which an end-user gains clarifications on the decisions from an AI/ML model. The AI/ML models learn the underlying characteristics of the available data and subsequently try to classify, predict, or cluster new data. The stage of explainability refers to the period in the process mentioned above in which a model generates the explanation for the decision it provides. The stages are found to be ante-hoc and post-hoc [264]. Brief descriptions of the categorised methods based on these stages are:  
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* Ante-hoc methods generally consider generating the explanation for the decision from the very beginning of the training on the data while aiming to achieve the optimal performance. Mostly, explanations are generated using these methods for transparent models, such as, Fuzzy models, Tree-based models, etc. * Post-hoc methods comprise an external or surrogate model and the base model. The base model remain unchanged, and the external model mimics the base model's behaviour to generate an explanation for the users.