## 2 Literature Review
### Explainable Artificial Intelligence (AI) in Churn Analysis  
While the application of machine learning algorithms has brought many benefits to business, it has also revealed several drawbacks. For example, in Customer Relationship Management, it is essential that every decision is meaningful. Advanced data analytics should enable decision-makers to understand the reason underlying the model's prediction of customer behavior results so that they can tailor their decisions and adjust personalized marketing strategies accordingly [4]. Decision-makers are often wary of or even reject AI systems because the performance of the predictive model or system is overemphasized while interpretability or transparency is ignored [33]. Many popular ML models are considered as \"black boxes\" [7; 34], meaning that the inner workings of the algorithms and associated decisions or classifications made are secretive. For example, individual predictions often lack interpretability when predicting a potential customer's risk of discontinuuing a telecommunications service. It is difficult to relate the predicted probability of customer churn to customer characteristics, which creates challenges in determining customer retention success and deciding on refinement strategies. Additionally, from a legislative perspective, the interpretability of AI algorithm conclusions is itself a right of consumers and regulators have a responsibility to ensure this right. Studies have discussed data transparency and ethical issues from the perspective of users or data subjects [35]. To address this problem, explainable AI (XAI) models are required to provide details or interpretations that make the operations of AI understandable. According to [36; 37], explainable AI enables interested parties to comprehend the primary influences on model-driven decisions as well as how AI algorithms carry out their operations, forecast outcomes, and make decisions. The _European General Data Protection Regulation_ (GDPR) emphasizes that intelligent decisions should be accompanied by relevant information about the logic involved and the significance and possible effects of their processing for the data subject. Therefore, the GDPR grants data subjects the right to obtain pertinent information regarding the basis for smart decision-making [38]. A few XAI approaches have been used for customer churn predictive models in the existing literature, including global surrogate models, Partial Dependence Plots (PDPs), Accumulated Local Effects (ALEs) [4], LIME [39], and Shapley values [40; 41]. In the work of [4], a number of XAI methods were employed to explain the customer churn predictive model by using a dataset of the telecom industry. The discussed global explanation methods included Partial Dependence Plots (PDPs), Accumulated Local Effects (ALEs), global surrogate models, and Shapley Valuea, as well local methods, including Individual Conditional Expectation (ICE) and the local surrogate model (LIME). The results show that a thorough understanding of the data, the inner workings of the model, and the issue can be addressed by the combination of several interpretations and approaches in a specific way. Scientists [42] proposed a novel XAI framework to identify the most significant factors that influence a customer's decisions on purchasing or abandoning non-life insurance coverage. Their framework applied similarity clustering to the Shapley values from a predictive model created by XGBoost. The results showed that the integration of the clustering technique and Shapley values effectively grouped the customers, which was used to forecast the churn decisions of customers. Researchers [7] provided an XAI solution for predicting customer churn and outputting its explanations through visualization. This system integrates two major methods, the Random Forest algorithm used to train a churn classifier and Shapley values used to provide global and local explanations for the churn classifier. Using LIME and Shapley techniques, Ref.