# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Year** & **XAI Framework** & **Output Format** & **Use Cases** & \
\begin{tabular}{c} **Programming** \\ **Language** \\ \end{tabular} & **ML/DL** & **Model Complexity** &
\begin{tabular}{c} **Open Source/** \\ **Commercial** \\ \end{tabular} & **Licence Type** \\ \hline
2019 & Skater [1] & Text, Visualizations &
\begin{tabular}{c} Understanding \\ Model behavior, \\ identifying/ing bias, \\ debugging models \\ \end{tabular} & Python & Framework Agostic & Flexible & Open Source & Apache 2.0 \\ \hline
2019 & CausalSHAPs [40] & \
\begin{tabular}{c} Feature Importance \\ with Visualizations \\ with the strongest \\ \end{tabular} &
\begin{tabular}{c} Understanding \\ Model behavior, \\ identifying/ing bias, \\ debugging models \\ \end{tabular} & Python & Primarily Focused on & Flexible & Open Source & Apache 2.0 \\ \hline
2018 & Anchors [42] & \
\begin{tabular}{c} Feature Importance, \\ Anchor set \\ \end{tabular} &
\begin{tabular}{c} Understanding \\ individual \\ predictions, \\ debugging models \\ \end{tabular} & Python & ML & Simple to Complex & Open Source & Apache 2.0 \\ \hline
2018 & Captum [43] & \
\begin{tabular}{c} Text, Visualizations \\ (integrated with various libraries) \\ \end{tabular} &
\begin{tabular}{c} Understanding \\ Model behavior, \\ identifying bias, \\ debugging models, \\ counterfactual explanations \\ \end{tabular} & Python & Framework Agostic, various PyTorch & Flexible & Open Source & Apache 2.0 \\ \hline
2018 & RISE [44] & \begin{tabular}{c} Visualizations \\ (Saliency maps) \\ \end{tabular} & \
\begin{tabular}{c} Understanding local \\ feature importance in \\ image classification \\ models \\ \end{tabular} & Python & Primarily focused on &
\begin{tabular}{c} Primary focused on \\ Image classification \\ models \\ \end{tabular} & Flexible & Open Source & MIT \\ \hline
2018 & INNvestigate [45,46] & \
\begin{tabular}{c} Text, Visualizations \\ (integrated with \\ Marplotlib) \\ \end{tabular} &
\begin{tabular}{c} Understanding \\ model behavior, \\ identifying/ing \\ important features, \\ debugging models \\ \end{tabular} & Python & Keras models & Flexible & Open Source & Apache 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Year** & **XAI Framework** & **Output Format** & **Use Cases** & **Programming Language** & **ML/DL** & **Model Complexity** & **Open Source/ Commercial** & **License Type** \\ \hline \multirow{4}{*}{2018 InterpretML [47]} & Textual explanations, and visualizations (including feature & Understanding model behavior, identifying & & & & & \\  & importance plots, partial dependence & debugging models, and improving & & & & & & Can handle models & \\  & SHAP values) & & & & & & & & \\ \hline \multirow{4}{*}{2017 SHAP [48]} & Textual explanations, and visualizations (including feature & Understanding model behavior, identifying & & & & & \\  & importance plots, partial dependence & & & & & & & \\  & topics, and and & and improving & & & & & & \\  & SHAP values) & & & & & & & \\  & model performance & & & & & & & \\ \hline \multirow{4}{*}{2017 SHAP [48]} & Textual explanations, and visualizations (including feature & Understanding model behavior, identifying & & & & & \\  & importance plots, and & & & & & & \\  & SHAP values) & & & & & & & \\  & model performance & & & & & & \\ \hline \multirow{4}{*}{2017 Grad-Cam [49]} & Textual explanations (including feature & Understanding feature importance, identifying bias, denoising models & & & & & \\  & SHAP values)