# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 4 Results
### Evaluating XAI  
The use of well-defined and validated metrics for evaluating the quality of XAI results (i.e., explanations) is of great importance for widespread adoption and further development of XAI. However, a significant number of authors still use XAI methods as a sort of add-on to their ML models and results without properly addressing the quality aspects of provided explanations, and only a few articles in our corpus use metrics to quantitatively measure the quality of their XAI results (Figure 11). More than 58% of the reviewed articles applied XAI but did not provide any evaluation of their XAI results (e.g., [65, 121, 170, 175, 186]). Among those that evaluated their XAI results, most relied on anecdotal evidence (20% of the reviewed articles, e.g., [185, 245, 249, 272, 280]). In approximately 8% of papers, the authors evaluated their XAI results by asking domain experts to evaluate the explanations (e.g., [66, 70, 89, 114, 167]). In approximately 19% of papers, however, some sort of quantitative metrics are used to provide the quality assessment (e.g., [94, 179, 187, 242, 281]). These numbers are in line with a recent review article about XAI evaluation methods that also highlighted the lack of reporting metrics to measure explanation quality, according to Nauta et al. [4], only one in three studies that developed XAI algorithms evaluates explanations with anecdotal evidence, and only one in five studies evaluates explanations with users. Also, Leite et al. state that \"evaluation measures for the interpretability of a computational model is an open issue\" [282]. To address this issue, they introduced an interpretability index to quantify how a granular rule-based model is interpretable during online operation. In fact, the gap of \"no agreed approach on evaluating produced explanations\" [283] is often mentioned as future work. Having such a metric would solve several XAI issues, such as decreasing the risk of confirmation bias [283, 284]. For this purpose, we further analyzed the articles that used metrics to measure explanation quality, primarily to see what the authors reported about the explainability of their results. Since different ML tasks and/or ML models may focus on different aspects, we divided the analysis according to the main task of the ML model. In the case when metrics have been used for evaluating the quality of clustering, segmentation, and other unsupervised ML methods' explanations, the findings highlight that the evaluated XAI approaches provided accurate, transparent, and robust explanations, aiding in the interpretation of the ML models and results (e.g., [285, 286]). Human and quantitative evaluations confirmed the methods' superiority in generating reliable, interpretable, and meaningful explanations [287], despite occasional contradictory insights that proved useful for identifying anomalies [269]. For the reinforcement learning applications, the findings of papers assessing their XAI results by evaluation metrics demonstrate that the proposed methods effectively explained complex models and highlighted the potential of Shapley values for explainable reinforcement learning [288]. Additionally, participants using the AAR/AI approach identified more bugs with greater precision [289], and while explanations improved factory layout efficiency, their interpretability remains an area for improvement [290]. The findings of the papers using regression as their main task, which used some metric to evaluate the explanations, underscore the critical role of explainability techniques like Shapley and Grad-CAM in enhancing model interpretability and accuracy (e.g., [157, 291]) across various domains, from wind turbine anomaly detection [244] to credit card fraud prediction [187]. While global scores aid in feature selection, semi-local analyses offer more meaningful insights [292]. XAI methods revealed system-level insights and emergent properties [293], though challenges like inconsistency, instability, and complexity persist [157, 294].