# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 5 Challenges
### Applicable Recommendations  
The increasing pervasiveness of ML models in our lives necessitates a deeper understanding of their decision-making processes. This is where XAI frameworks come in, with an aim to shed light on the \"black box\" nature of these algorithms. However, the current XAI frameworks face various limitations that hinder their effectiveness. This study proposes a series of recommendations to improve these frameworks and unlock their full potential. **Computational Efficiency is Key:** A significant hurdle for many XAI frameworks is their computational burden, especially when dealing with large datasets or complex models. Research into more efficient algorithms and hardware acceleration techniques is crucial. Frameworks like LIMEs, SHAPs, LOREs, and EBMs could benefit greatly from such advancements. **Interpretability Over-Complexity:** While some frameworks excel at technical explanations, the user-friendliness often suffers. Efforts to simplify the explanations or provide clear visualizations would significantly enhance the usability of frameworks like iNNvestigate, LRP, and Kernel SHAP, making them more accessible to non-experts. **Expanding the XAI Toolbox:** Many frameworks are limited to specific model types. Increased flexibility to handle a wider range of architectures would broaden their practical applications. Frameworks like Anchors, CEM, TCAV, EBMs, RISE, and TreeSHAPs could benefit from such expansion. **Scalability for the Real World:** The effectiveness of some frameworks diminishes with large datasets. Finding ways to scale frameworks like LOREs and GraphLIMEs efficiently is essential for real-world applications. **User-Centric Design:** Frameworks like Captum and InterpretML require additional setup and knowledge for implementation. Developing more user-friendly interfaces, along with comprehensive documentation and tutorials, would significantly lower the barrier to entry for beginners. **Beyond the Basics:** Several frameworks offer specific functionalities. For example, AIX360 excels at fairness analysis but lacks individual prediction explanations. Integrating these functionalities would provide a more holistic understanding of model behavior. Similarly, frameworks like Skater could benefit from expanding the model support and user education on counterfactual reasoning. \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 GraphLIMEs [39] & Computationally intensive, not ideal for real-time applications. Explains tabular data predictions by modeling feature relationships as graphs. & Especially useful when feature interactions are important \\ \hline DLIME [32] & Relatively new framework with less mature documentation and community support & Aims to combine global and local explanations using game theory and feature attributions for diverse model types \\ \hline InterpretML [47; 102] & Requires additional installation and integration to work. Might not be as user-friendly for beginners & Offers a diverse set of interpretability techniques as part of a larger ML toolkit \\ \hline Alteryx Explainable AI [38] & Commercial offering with potential cost implications & Provides various visual and interactive tools for model interpretability within the Alteryx platform \\ \hline TINTRules [29] & The black-box nature may limit the collaborative tuning process, potentially introducing biases in decision-making & aiming to generate high-quality explanations through multiobjective optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
**Collaboration is Key:** For frameworks like CIAMPs and DALEX, with limited publicly available information, fostering community involvement and research could lead to significant advancements. **Balancing Power and Ease of Use:** Frameworks with powerful functionalities, like OAK4 XAI and DeepLIFT, often require careful hyperparameter tuning.