# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 3 Related Works
### Explainable AI in CVSS Prediction  
While these results are impressive, there remains hesitancy in deploying these models and utilizing them to assist analysts. This hesitation stems from a general lack of trust in Deep Learning models due to their opacity and lack of interpretability. The inability to question or examine the inner workings of these models means that we cannot be certain if they have learned the correct information. A model that has learned incorrect biases can still produce accurate results. Blindly trusting such a model can lead to issues as the underlying reasoning behind its conclusions may be flawed, even if the actual predictions are accurate. This is where eXplainable AI (XAI) becomes valuable. XAI focuses on making AI systems more transparent and explainable, particularly in terms of how the model's classification decisions are made. However, for these systems to be fully trusted, both the decision-making process must be understandable and the features used by the model must be interpretable. While XAI helps clarify decision making, the interpretation of specific features requires the expertise of domain specialists, as the meaning of these features may vary depending on the context. If models had an explainable component, analysts would indeed be able to place trust in them and utilize them for future cases. Some of the works mentioned earlier, such as [4; 9; 10; 11], have taken this into account and incorporated explainable AI (XAI) algorithms to illustrate what their models have learned. For instance, in [PERSON] et al. 's work [4], the authors engineered new features related to time differences and the number of tokens. They then examined the impact of each feature by exporting the weights of their SVM model. The result was a table indicating the importance of each feature in relation to the metadata surrounding each vulnerability rather than the actual description. However, the XAI aspect of this approach is limited to the meta-features and does not extend to the text descriptions. As a result, analysts still need to manually perform the full analysis. In the case of [PERSON] et al. 's work [2], the SHAP algorithm was utilized to plot the class affinity of different words, showcasing how much a word correlates with a specific class. The authors compared these affinities across different preprocessing techniques to visualize the impact of preprocessing. The final result, as shown in Figure 5, is a binary bar plot demonstrating the impact on each class. However, using SHAP in this manner is limiting because it calculates the class affinity impact of the preprocessing methods rather than the text description itself, which is what humans typically focus on. Furthermore, the plot only works for binary classes, whereas the problem at hand involves multiple multi-class metrics. These limitations highlight the need for more comprehensive XAI approaches that directly address the text descriptions' interpretability and consider the metrics' multi-class nature. In [PERSON] et al. 's work [11], Layer Integrated Gradients (LIG) from the Captum library [42], a PyTorch XAI library, were employed to calculate the word class affinity for a set of input descriptions. The resulting visualization, depicted in Figure 6, assigns colors to words based on the intensity of their affinity to a particular class. Each plot corresponds to a single sentence, rendering them local explanations. However, it should be noted that these explanations only pertain to binary classes, as indicated by the two colors: green for class 1 and red for class 0. Since multiple CVSS metrics involve multi-class classification, this approach has a limitation. To address this, the authors generated multiple plots, each comparing one class against all the others in a one-versus-all classification scheme. While this is a commendable attempt to tackle the problem, there remains the challenge of defining decision boundaries for each class relative to the others.