# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Year** & **XAI Framework** & **Output Format** & **Use Cases** & \
\begin{tabular}{l} **Programming** \\ **Language** \\ \end{tabular} & **ML/DL** & **Model Complexity** &
\begin{tabular}{l} **Open Source/** \\ **Commercial** \\ \end{tabular} & **Licence Type** \\ \hline
2016 & LRP [57] & Relevance scores & \
\begin{tabular}{l} Understanding \\ individual \\ predictions in deep \\ neural networks \\ \end{tabular} & Python &
\begin{tabular}{l} Deep neural \\ networks \\ \end{tabular} & Simple to complex & Open Source & Varies \\ \hline
2016 & What-IF Tool [58] & \begin{tabular}{l} Feature \\ contributions, \\ visualizations \\ \end{tabular} & \
\begin{tabular}{l} Exploring \\ hypothetical \\ scenarios, \\ Debugging models \\ \end{tabular} & Python/ &
\begin{tabular}{l} TensorFlow/ \\ JavaScript \\ \end{tabular} & Various & Simple to complex & Discontinued & N/A \\ \hline
2016 & AIX360 [65] & Text, Visualizations &
\begin{tabular}{l} Understanding \\ identified, \\ debugging models \\ \end{tabular} & Python & Framework agnostic & Flexible & Open Source & Apache 2.0 \\ \hline
2016 & EBMs [60] & \begin{tabular}{l} Text, Visualizations \\ (partial dependence \\ plots, feature \\ importance) \\ \end{tabular} & \
\begin{tabular}{l} Understanding \\ model behavior, \\ identifying feature \\ interactions, \\ debugging models \\ \end{tabular} & Python &
\begin{tabular}{l} Gradient boosting \\ models \\ \end{tabular} & Flexible & Open Source & Varies \\ \hline
2015 & ELI5 [71] & \
\begin{tabular}{l} Textual explanations, \\ Arguments, \\ Visualizations \\ \end{tabular} &
\begin{tabular}{l} Understanding \\ predictions, \\ Classification, \\ Tabular \\ \end{tabular} & Python & Various & Simple to complex & Open Source & Varies \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._Figure 2: A standardized XAI framework and its components. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**XAI Framework** & **Text** & **Visualization** & **Feature Importance** & **Arguments** \\ \hline TNTRules & ✓ & ✓ & & \\ \hline CIAMP & ✓ & ✓ & & \\ \hline LOREs & ✓ & & & ✓ \\ \hline DLIME & ✓ & ✓ & & \\ \hline OAK4 XAI & ✓ & ✓ & & \\ \hline TreeSHAPs & ✓ & ✓ & & \\ \hline DALEX & ✓ & & ✓ & \\ \hline CEM & & & & ✓ \\ \hline Alteryx & & ✓ & & \\ \hline GraphLIMEs & ✓ & ✓ & ✓ & \\ \hline Skater & ✓ & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparative analysis of XAI framework output formats. The information in Table 6 enables a more precise analysis of how XAI frameworks handle model complexity. The table categorizes these frameworks into flexible, simple to complex, and complex. Flexible frameworks are likely versatile tools, adept at explaining models across a spectrum of complexity, from basic to intricate. These frameworks might offer a comprehensive range of explanation techniques (text, visualization, feature importance, or arguments) that can be tailored to the specific model being analyzed. The frameworks categorized as simple to complex might be the most effective for explaining models that range from relatively straightforward to moderately complex. These frameworks could balance interpretability and the ability to handle some level of model intricacy. Finally, complex frameworks are likely designed to tackle the challenge of explaining highly complex models, particularly deep learning models with numerous layers and non-linear relationships between features and predictions. These frameworks might employ more advanced techniques specifically suited to provide insights into these intricate models. It is important to remember that the specific capabilities of each XAI framework can vary. To obtain a complete picture, it is always recommended to identify the strengths and weaknesses of each framework, particularly their suitability for different model complexities. We will be looking into this in Section 5.