# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline  & & Highly performing black-box AI models can be insufficient if they make predictions based on the wrong features. Interpretability gives information on a model’s decision-making process and so can lead to the development of better AI models. Interpretability is crucial in safety-critical fields (like medical) and when finding new information (like physics research). \\ \hline  & & The authors mentioned that the use of explainability can improve user experience and trust by providing clear and understandable explanations of the system’s behavior. And also, they mentioned it can lead to great acceptance and adoption by users of the systems. They highlighted that the explainability in the smart home domain is essential due to the sensitive and high-risk nature of some AI applications that are closely related to human lives, wellness, and safety. \\ \hline  & & The authors mentioned that explainability increases the user’s confidence in the decision-making process with existing ML models that are limited to glaucoma prediction. And also, they mentioned that explainability provides convincing and coherent decisions for clinicians/medical experts and patients. \\ \hline  & & Explainability increases trust towards AI systems and safety for use in the medical field. Explainability should also be measurable so the explanations can be trusted. \\ \hline  & & Gas production systems are complex, and black-box methods are used for product gas prediction for that reason. Production systems can fail without anyone knowing why. Explainability would increase the use of AI and increase safety and efficiency due to increased knowledge of the system. \\ \hline  & & It is difficult for clinical practitioners to adopt highly developed AI systems due to their lack of interpretability. There are lots of great tools for brain disease prediction that could help diagnose illness in its early stages. Explainability would allow people with little to no knowledge of AI to use these diagnostic tools. \\ \hline  & & \\ \end{tabular}
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Table 1: _Cont._
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline \multirow{9}{*}{Tao et al. (2023)[228]} & & Lack of interpretability in black-box models hinders the development of the models and use of AI in online gaming. Explainability is needed to make sure models are learning the right relations, allow practitioners to adjust the model in problematic cases, make sure that models perform the same way in an online setting, and enable easy debugging. \\ \cline{2-3}  & & The authors mentioned that explainability in the context of Vision Transformers is essential for ensuring transparency, mitigating biases, enhancing safety, and promoting trust in AI systems. \\ \cline{2-3}  & & In manufacturing industries, data is collected straight from the machines and processed with AI to provide information about the machines to help making decisions. Decision makers usually aren’t experts of AI, so they cannot rely fully on AI-made decisions. Explainability would make relevant decision-making easier and adopting AI in decision-making processes more worthwhile. \\ \cline{2-3}  & & As ML models become more widely used, explainability is needed so the right decisions can be made based on AI-made decisions. There are several methods to analyze an ML model’s performance, but those don’t give insight on the decision-making process. \\ \cline{2-3}  & & Explainability is needed in complex ML systems that do face verification so that ML-made decisions can be trusted. False-positive results on face recognition used in security applications are a big threat to security and privacy. Interpretability increases users’ trust and helps develop better and more accurate models.