# 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 \hline Qaffas et al. (2023)[202] & Inventory management & The authors mentioned that providing explanations for the assignment of items to classes A, B, and C allows for a better analysis of the items, easy detection of misclassifications, improved understanding of inventory classes, and flexibility in inventory management decisions. And also they mentioned explainability helps make decisions more transparent and enhances interpretability. \\ \hline Wang et al. (2023)[317] & Improving performance of XAI techniques for image classification & Use of black-box models in critical fields is increasing, and explainability is much needed to help users evaluate models’ decisions and increase trust from users. The efficiency and accuracy of modern XAI visual explanation methods (CAM, LIME) can be improved, which is the goal of this study. \\ \hline Mahbooba et al. (2021)[173] & Trust management in intrusion detection systems & The authors mentioned that human experts need to understand the underlying data evidence and causal reasoning behind the decisions made by AI in their domain. Further, the network administrators can enforce security policies more effectively for identified attacks, leading to improved trust in the systems by providing explanations. \\ \hline Puechmorel (2023)[318] & Creating better XAI techniques with manifolds and geometry & Data manifolds are not much researched, even when computing on manifolds can result in higher performance and the ability to compute on very high-dimensional data. \\ \hline Rozanec et al. (2021)[166] & Prediction of pentane content during liquefied petroleum gas debutanization process & Explainability is important so users can understand the model's limitations in operational use. \\ \hline Heuillet et al. (2022)[288] & Explaining reinforcement learning systems & Explainability also increases trust toward AI systems from the user end. Reinforcement learning is an ML technique that is increasingly used in critical fields where the interpretability of the ML model is necessary for the end users. \\ \hline \end{tabular}   
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline  & & ML models used for credit score/loan risk prediction in finance are becoming increasingly complex, which is contrary to increasing demand for transparency from authorities. As loan default is more costly to businesses than the unaccepted possible clients, the most beneficial model might not be the same as the most optimal model; interpretability is needed to aid in developing these models. \\ \hline Mohamed et al. (2022) [275] & Small-object detection & Explainability increases reliability and therefore accelerates the ML model’s approval for real-life applications. \\ \hline  & & There is a growing need to understand the relationships between features and predictions in black-box models. The Great Lakes area is so big (84\% of North America’s surface water) that it impacts the environment very differently than “regular”