# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 5 Challenges
While they provide deeper insights than just correlation, interpreting their inner workings might be challenging. DeepLIFT [67; 68] and ATTN [54] focus on explaining the specific aspects of models, like individual neurons or attention mechanisms, but may not be suitable for all tasks [101]. DALEX allows for \"what-if\" scenarios to see how input changes affect the predictions, but it might have limited documentation [36]. ELI5 offers clear explanations for specific models but has limited applicability [71]. GraphLIMEs excel at explaining models where feature interactions are important but can be computationally heavy [39]. The DLIME [32] and InterpretML [47; 102] provide various explanation techniques but require additional setup or might have limitations regarding the model types or user-friendliness. Finally, Alteryx Explainable AI offers a commercial toolkit with visual and interactive tools for interpretability [38]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**XAI Framework** & **Key Limitations/Challenges** & **Primary Strengths** \\ \hline LIMEs [56] & May not fully reflect complex model behavior, computationally expensive for large datasets & Simple, intuitive explanations, work with various models \\ \hline SHAPs [48] & Computationally expensive, explanations can become complex for large feature sets & Grounded in game theory, offers global and local insights \\ \hline Anchors [42] & Reliance on rule generation may not cover all decision boundaries & Provides high-precision, human-understandable rules \\ \hline LOREs [31] & Reliance on an external rule generator; may not scale well with large datasets & Can explain decisions using existing rules, offering fast results \\ \hline Grad-CAM [49] & Primarily for convolutional neural networks (CNNs); they may not work well with other architectures & Efficiently visualizes heatmaps for image classification \\ \hline Integrated Gradients [50] & Relies on choosing a baseline input, can be computationally expensive & Offers smooth visualizations of feature attributions \\ \hline CEM [51] & On choosing an appropriate reference class, which may not be intuitive for complex models & Offers class-specific explanations, interpretable through saliency maps \\ \hline LRP [57] & Computational costs can be high for complex models, explanations can be complex to interpret & Provides comprehensive attribution scores for each input feature \\ \hline What-If Tool [58] & Requires expert knowledge to design effective explanations, limited to specific model types & Allows interactive exploration of model behavior through hypothetical scenarios \\ \hline ALX360 [59] & Primarily focused on fairness analysis; may not provide detailed explanations for individual predictions & Offers various tools for detecting and mitigating bias in models \\ \hline Skater [1] & Limited model support, requires knowledge of counterfactual reasoning & Enables counterfactual explanations, providing insights into “what-if” scenarios \\ \hline Captum [43] & Requires familiarity with the library’s API, not as beginner-friendly as other options & Offers a variety of explainability techniques in a unified Python library \\ \hline \hline \end{tabular}
\end{table} 
Table 7: XAI framework challenges and strengths comparison.