# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 5 Challenges
### New Proposed XAI Framework  
In this section, we propose a novel framework, termed eXplainable AI Evaluator (XAIE), designed to comprehensively evaluate and compare the existing XAI techniques. Currently, limitations exist in objectively assessing the strengths and weaknesses of various XAI methods. XAIE addresses this gap by establishing a standardized approach for cross-framework comparisons among diverse tasks and ML models. Key Features:  
_Unified Benchmarking Suite_: We propose the development of a comprehensive suite of benchmarks encompassing various XAI evaluation aspects. These benchmarks will assess the factors critical to XAI effectiveness, including the following:  
* Fidelity: Measures how accurately the explanation reflects the true decision-making process of the underlying AI model. * Fairness: Evaluates whether the explanation highlights or perpetuates any biases present within the model. * Efficiency: Analyses the computational cost associated with generating explanations using the XAI method. * User-Centricity: Assesses the understandability and interpretability of the explanations for users with varying levels of technical expertise. _Standardized Explanation Representation_: To facilitate meaningful comparisons, XAIE will define a common format for the explanations generated by different XAI techniques. This standardized format will allow for consistent evaluation and analysis across diverse frameworks. _Modular Design_: To accommodate the evolving nature of the XAI field, the framework will be designed with a modular architecture. This modularity enables the seamless integration of new XAI methods and the incorporation of novel evaluation metrics as the research progresses. Workflow: The XAIE framework will follow a user-driven workflow for XAI evaluation and comparison:  
User Input:  
Users will specify the XAI methods they wish to compare. Details regarding the target AI model and its designated task (e.g., image classification or loan approval prediction) will also be provided by the user. Evaluation Process:* The XAIE framework will execute each selected XAI method on the target model, prompting the generation of explanations. * The generated explanations will then be evaluated against the chosen benchmarks from the comprehensive suite. Output:  
* Upon completion, the framework will deliver a comprehensive report comparing the XAI methods based on the evaluation results. This report will include the following:
* A detailed breakdown of the strengths and weaknesses of each XAI method for the specific model and task. * Visual representations of explanation fidelity and user-friendliness. * Recommendations for the most suitable XAI method based on user priorities (e.g., prioritizing explainability over efficiency). Figure 4 illustrates the flow chart that explains the processes involved in the new proposed XAIE model. XAIE offers several advantages. Firstly, it promotes standardization in XAI evaluation. This consistency enables a more unified understanding of how effective different XAI techniques are. Secondly, XAIE empowers users to make informed decisions. By providing a platform for comparing various XAI methods, the users can select the most suitable technique for their specific needs and applications. Finally, XAIE acts as a valuable tool for  
Figure 4: The workflow model of XAIE framework. researchers. The framework facilitates the comparison and improvement of the existing XAI methods, ultimately accelerating the advancements in the field. However, developing XAIE also presents some challenges. Reaching a consensus on a common explanation format and a comprehensive set of evaluation metrics can be difficult due to the inherent diversity among the XAI methods. Additionally, running multiple XAI techniques on complex models can be computationally expensive. This necessitates exploring optimization techniques to ensure that the framework executes efficiently. Looking towards the future, the development of XAIE presents exciting opportunities.