# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 3 Related Works
### Overview of Related Work
This becomes particularly crucial when dealing with close importance scores. Moreover, it poses challenges for individuals with colorblindness, who may encounter difficulty in distinguishing between various colors. A more effective approach involves the utilization of numerical values instead or in addition to depicting word impact, offering a clearer and more objective representation. This not only ensures fairer comparisons between words but also enhances accessibility for individuals with visual impairments. * The use of feature attribution: It can be argued that relying solely on feature attribution for inference may not be sufficient and should be complemented with other information such as word distribution between classes or model confidence scores. Feature attribution methods provide insights into the importance of individual features or words, but they do not capture the complete picture of the model's decision-making process. By considering additional factors such as word distribution between classes, one can gain a better understanding of how different words contribute to the classification of each class. Similarly, incorporating the model's confidence scores can help evaluate the reliability of the predictions and make more informed decisions based on the explanations. Therefore, combining feature attribution with other relevant information can enhance the interpretability and reliability of the explanations. Table 4 summarizes the related works' explanations based on the criteria discussed earlier and compares them to our method. All in all, the current body of research has successfully established a solid and diverse foundation for understanding the capabilities of explainable artificial intelligence (XAI) in the context of predicting CVSS scores. This body of work has enabled the identification of several key criteria for effective explainability, including class coverage (supporting all numbers of class values), the nature of explanations (descriptive vs. inferable), the format of explanations (image vs. numeric), robustness (considering factors like word frequency and outliers), and the chosen method (e.g., feature attribution, plain statistics). These findings have served as inspiration for our own work, driving us to build upon past research and develop an XAI approach that specifically addresses the needs of analysts during the analysis phase. By doing so, we aim to provide explanations that are truly helpful and practical in assisting analysts in their decision-making processes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Multi-Class** & **Nature of** & **Explanation** & **Coloring-Based** & **Explanations** & **Comparative** \\  & **Coverage** & **Explanations** & **Format(s)** & **Explanation** & **Based on** & **Analysis** \\ \hline
[2] & None & Descriptive & Images & Yes & Feature attribution & No \\ \hline
[11] & “+” & Descriptive & Images & Yes & Feature attribution & No \\ \hline
[10] & “++” & Descriptive + & & Tables & No & Feature attribution & No \\ \hline \multirow{2}{*}{Ours} & “++” & Descriptive + & & Images, tables, rules & No &
\begin{tabular}{c} Feature attribution, \\ word frequency, \\ statistics, \\ model confidence \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of explanation criteria. the “+” sign is the degree of coverage, ranging from None to “++”.