# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 4 Results
### Intrinsically Explainable Models  
As shown in Figure 9, the majority of recent XAI papers used post-hoc explainability approaches on ML models, which are not naturally easily interpretable (79%), as opposed to the intrinsically explainable models (12%); other papers (9%) reported a combination of both. Figure 10 presents the distribution of intrinsically explainable ML models. From all the reviewed XAI papers that reported their used method as intrinsically explainable, the majority were tree-based (41%), followed by deep NN (19%), linear or logistic regression (5%), and some Bayesian models (3%). The predominance of tree-based ML models could have been expected, as well as a relatively high number of linear and logistic regression models, which both are considered naturally transparent and simpler to understand, given their inherent interpretability. On the other hand, the relatively high number of deep neural networks that have been represented as intrinsically explainable is somewhat surprising. Figure 8: The main ML tasks in the papers in our corpus (all other ML tasks are used in only one or at most two papers). Figure 7: Mostly used ML models in the papers in our corpus (only ML models used at least five times are shown). There are significant differences between different ML models represented as intrinsically explainable with regard to the form of explanation they use. While the intrinsically explainable tree-based ML models use a variety of forms of explanation, including feature importance (in 50% of all cases), rules (38%), and visualization (31%), the deep NN models being reported as intrinsically explainable rely mainly on visualization (in more than 67% of all cases). The intrinsically explainable linear and logistic regression ML models, however, use predominantly feature importance as their form of explanation (in 75% of all cases). In the most frequent XAI application domain, namely health, the use of tree-based ML models is predominant, as the tree-based models are used in 28% of all health applications, followed by (deep) neural networks (22%), and interestingly fuzzy logic (11%), while all other models were used only once in health. Given the known history of the development of ML methods in the field of medicine and healthcare, where the ability to validate predictions is as important as the prediction itself, and consequently the key role of decision trees [277], this result does not even surprise us. With regard to other application domains, we can see that intrinsically explainable ML models, like tree-based models and linear or logistic regression models, are used for finance and education applications much more often than other ML models. While the financial domain represents only 1% of (deep) neural network applications, it represents 6% of all tree-based ML model applications (used for credit risk estimation [192; 193], risk manage  
Figure 10: Number of papers that used a specific ML model, which is presented as intrinsically explainable. Figure 9: Number of papers in our corpus that used a post-hoc approach versus intrinsically explainable ML model. ment in insurance [195], financial crisis prediction [194], investment decisions and algorithmic trading [189], and asset pricing [186]) and even 9% of linear or logistic regression applications (used primarily for credit risk assessment [190] and prediction [193], as well as financial decision-making processes [189]). While post-hoc explainability methods, primarily SHAP and LIME, are the most favored in the financial sector [189], intrinsically explainable modes are gaining popularity for revealing the insights and are being used for stock market analysis [278] and forecasting [279], profit optimization, and predicting loan defaults [191].