## 4 Artificial Intelligence Algorithm  
The LIME algorithm [8; 9; 10], works in the following manner: first, it selects a random subset of instances from the dataset that represents the population of interest. Next, it trains a black-box model, such as a neural network or support vector machine, on the dataset. This model should be able to make accurate predictions with new, unseen data. Then, it chooses an instance from the dataset that we want to explain. It creates a new dataset by sampling instances similar to the instance which we want to explain. This can be achieved by perturbing the instance, such as by adding noise or dropping features, to create new instances [11; 12]. The selection of LIME as an explainability method in AI models, in preference to other techniques, hinges on the specific context and objectives of the application. LIME's inherent model-agnostic nature makes it an appealing choice, as it can be seamlessly applied to a wide array of machine learning models, including complex black-box ones (which is illustrated in detail in Figure 4), without needing prior knowledge of the model's architecture. Additionally, LIME specializes in providing localized explanations for individual predictions, catering to scenarios where understanding the rationale behind a single prediction holds more significance than explaining the entire model's behavior. This becomes especially beneficial in applications where high-stakes decisions rely on individual instances, as it allows for pinpointed insights into why a specific prediction was made. Furthermore, the simplicity of LIME-generated explanations, which approximate intricate models with more interpretable ones, offers clarity and ease of understanding, which can be pivotal in scenarios where stakeholders and end-users require straightforward, human-intuitive explanations. On the other hand, the choice of LIME is also influenced by its transparency, as it reveals the most influential features behind a given prediction. This feature is vital in domains like healthcare and finance, where comprehending the impact of different input variables is of paramount importance. LIME is instrumental in error detection, enabling the identification of errors and biases in AI models, a crucial step towards ensuring fairness and accuracy. Moreover, in applications where human experts are actively involved in the decision-making process and need to both understand and potentially correct model predictions, LIME's capacity to generate localized explanations fosters effective collaboration between human expertise and AI capabilities. Lastly, LIME helps to meet regulatory requirements that demand explanations for AI model decisions, making it a prudent choice for compliance in heavily regulated sectors. Nevertheless, the selection of the most suitable explainability method should always be contingent on the specific use case, the nature of the AI model, and the unique needs of the stakeholders, as other methods like SHAP, integrated gradients, and decision trees may  
Figure 4: Unboxing the black box for the urology dataset using LIME. offer distinct advantages in differing contexts. Evaluating the strengths and limitations of each approach is key to ensuring that the chosen method aligns with the interpretability prerequisites of the application at hand. For our case, after several analyses related to these techniques, we came to the conclusion that the most advantages are provided by the use of LIME. This method excels in providing localized explanations. It focuses on explaining individual predictions rather than the global behavior of the model. This granularity is especially valuable in situations where understanding the rationale behind specific predictions is more important than having a holistic view of the model's behavior. For instance, in healthcare, it can offer insights into why a particular diagnosis was made, which can be vital for both medical professionals and patients.