## 6 Discussion
### Open Issues and Future Research Direction  
One of the objectives of this study was to sort out the open issues on developing explainable models and propose future research directions for different application domains and tasks. On the basis of the studies presented in the selected articles for this SLR, it was observed that the proposed methodologies' major limitation lies with the evaluation of the explanations. The studies addressed this issue with different techniques of user studies and experiments. However, there is still an urgent need for a generic method for evaluating the explanations. Another observed issue was algorithm-specific approaches of adding explainability. It is an obstacle to making the established systems in action explainable. Additionally, there remain other open issues to be addressed. Based on the observed shortcomings of prevailing explainable models, several possible research directions are outlined below:  
* It is evident in the findings of the study that safety-critical domains and associated tasks are most facilitated with the development of XAI. However, less investigation was performed for other sensitive domains, such as the judicial system, finance and academia, in contrast with the domains of healthcare and industry. Further exploitation of the methods can be performed for the less developed domains in terms of XAI;
* One of the promising research areas in the domain of networking is the Internet of Things (IoT). The literature indicates that several applications such as anomaly detection [183] and building information systems [184,185] for IoT have been facilitated by agent-based algorithms. These applications can be further associated with XAI methods to make them more acceptable to end-users;* The impact of the dataset (particularly the effect of dataset imbalance, feature dimensionality, different types of bias problems in data acquisition and dataset, etc.) on developing an explainable model can be assessed through studies;
* It was observed that most of the works were performed done for neural networks and through post hoc methods, explanations were generated at the local scope. Similar cases were also observed for other models, such as SVM and ensemble models, since their inference mechanism remains unclear to users. Although several studies have shown approaches to produce explanations at a global scope by mimicking the models' behaviour, they lack performance accuracy. More investigations can be carried out to produce an explanation in a global scope without compromising the models' performance for the base task;
* The major challenge of evaluating an explanation is to develop a method that can deal with the different levels of expertise and understanding of users. Generally, these two characteristics of users vary from person to person. Substantial research is needed to establish a proper methodology for evaluating the explanations based on the intended users' expertise and capacity;
* User studies were invoked to validate explanations based on natural language, in short, textual explanations. Automated evaluation metrics for textual explanations are not yet prominent in the research works;
* Evaluating the quality of heatmaps as a form of visualisation is still undiscovered beyond the visual assessment technique. In addition to heatmaps, evaluation metrics for other visualisation techniques, e.g., saliency maps, are yet to be defined.