# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 5 Challenges  
XAI is characterized by a variety of frameworks, each with unique strengths and limitations (see Table 7). LIMEs provide a straightforward approach, understandable explanations, and is compatible with a variety of models, although it may find it difficult to completely capture complicated model behavior and may be computationally expensive for large datasets [56]. Although computationally demanding, especially for models with large feature sets, SHAPs offer both local and global explanations with a foundation in game theory [89]. Although they depend on rule creation and might not cover every decision boundary, anchors are excellent at producing precise, intelligible rules for humans [42]. Despite their reliance on an external rule generator and potential scalability issues with large datasets, LOREs provide quick results by effectively explaining the decisions using pre-existing rules [31]. Despite being primarily intended for convolutional neural networks (CNNs), Grad-CAM effectively visualizes heatmaps for image classification tasks [49]. Although they provide smooth feature attribution representations, Integrated Gradients can be computationally costly and require the selection of a baseline input [50]. Although it depends on selecting the right reference class, the CEM provides class-specific explanations that may be understood using saliency maps [37]. Notwithstanding the perhaps large computing costs and intricate interpretations, LRP offers thorough attribution ratings for every input characteristic [90; 91]. The What-If Tool is a tool that facilitates the interactive study of model behavior through hypothetical situations. However, it is limited to certain model types and requires expertise to develop meaningful explanations [58]. Although AIX360 is primarily concerned with fairness analysis, it does include several tools for identifying and reducing the bias in models [59; 65]. It may not offer comprehensive explanations for specific forecasts. Despite its limited model support, Skater allows for counterfactual explanations, offering perspectives on \"what-if\" circumstances [92]. Although it may require expertise with its API, Captum provides a range of explainability strategies in a single Python module [93]. The DLIME provides a deterministic substitute appropriate for real-time applications, albeit being less interpretable than the original LIMEs and restricted to tabular data [94]. Despite its primary focus on picture classification problems, TCAV produces interpretable visualizations utilizing idea activation vectors [10]. Explainable Boosting Machines (EBMs) are restricted to decision tree-based models, although they help with feature significance analysis [95; 96]. Despite focusing mostly on image classification challenges, RISE finds significant picture areas to produce interpretable heatmaps [44]. Although Kernel SHAPs rely on computationally costly kernel functions, it provides flexibility in managing complicated feature interactions [97; 98]. CIAMPs and TNTRules seek to explain model predictions; nevertheless, some constraints and difficulties might not be easily accessible [30]. Although it depends on locating optimum anchors, which may be computationally costly, OAK4 XAI seeks to develop succinct and understandable anchors using game theory techniques [33]. Although explanations for highly complicated decision trees may become difficult, TreeSHAPs provide clarity on how tree-based algorithms arrive at predictions [99]. By providing academics  
Figure 3: Application domains for different XAI frameworks. and practitioners a wide range of tools for comprehending and evaluating ML models, these frameworks work together to advance XAI. Several techniques exist to understand how ML models arrive at their predictions. Some methods, like CasualSHAP [100] and INNvestigate [45], delve into the causal relationships between features and outcomes but require additional data and can be computationally expensive.