# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 5 Challenges
\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 Explain-IT [41] & The clustering step through supervised learning may result in introducing bias due to the application of a specific model & provides improved insights into unsupervised data analysis results, enhancing the interpretability of clustering outcomes \\ \hline TCAV [55] & Primarily for image classification; may not be applicable to other domains & Generates interpretable visualizations through concept activation vectors \\ \hline EBMs [60] & Limited to decision tree-based models, can be computationally expensive for large datasets & Offers inherent interpretability through decision trees, facilitates feature importance analysis \\ \hline RISE [44] & Primarily for image classification tasks; may not generalize well to other domains & Generates interpretable heatmaps by identifying relevant regions of an image \\ \hline Kernel SHAPs [34, 48] & Relies on kernel functions, which can be computationally expensive and require careful selection & Offers flexibility in handling complex relationships between features through kernels \\ \hline CIAMPs [30] & Limited publicly available information about specific limitations and challenges & Aims to explain model predictions using Bayesian hierarchical clustering \\ \hline OAK4 XAI [33] & Relies on finding optimal anchors, which can be computationally expensive & Aims to identify concise and interpretable anchors using game theory concepts \\ \hline TreeSHAPs [34] & Primarily focused on tree-based models, limiting its usage with other model architectures. Explanations for very complex decision trees can become intricate and challenging to interpret. & Offers greater clarity in understanding how tree-based models arrive at predictions. \\ \hline CasualSHAPs [40] & Requires access to additional data and assumptions about causal relationships, which might not always be readily available or reliable. Can be computationally expensive, especially for large datasets. & Provides potential insights into which features drive outcomes, going beyond mere correlation. \\ \hline INNVestigate [45, 46] & While aiming for interpretability, the internal workings of the framework itself might not be readily understandable for non-experts. & Explanations for intricate deep learning models might still require some expertise to grasp fully. Makes the decision-making process of “black-box” deep learning models more transparent. \\ \hline DeepLIFT [67, 68] & Hyperparameters can significantly impact the explanation results, requiring careful tuning. May not be suitable for all types of deep learning models or tasks. & Pinpoints the importance of individual neurons within a neural network, offering insights into its internal workings. \\ \hline ATTN [54] & Primarily focused on explaining attention mechanisms, limiting its use for broader model interpretability. Understanding attention weights and their impact on the model’s output can be challenging for non-experts. & Helps decipher how attention mechanisms are used in deep learning models, especially for NLP and computer vision tasks. \\ \hline DALEX [36] & Compared to some more established frameworks, DALEX might have less readily available documentation and resources. DALEX might still be under development, and its capabilities and limitations might evolve. \\ \hline ELI5 [71] & Limited to sci-kit-learn and XGBoost models. Might not generalize to all ML models & Provides clear, human-readable explanations for decision trees, linear models, and gradient-boosting models \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._