## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.5 Dimension V: Interpretability and Explainability  
AI models can be difficult to interpret and explain, which can make it difficult for organizations to understand how decisions are made. It is important to ensure that AI models are transparent and explainable. To address this challenge, organizations should implement interpretability and explainability controls such as feature importance analysis and model visualization tools. Figure 14 shows the challenging elements of interpretability and explainability. Artificial intelligence has become an integral part of modern society, with its influence seen in various domains, including healthcare, finance, transportation, and many others [150]. The rise of AI has been largely driven by advances in machine learning and deep learning techniques, which have demonstrated impressive results in solving complex problems [151]. However, these techniques have also given rise to \"black-box\" models, characterized by their lack of interpretability and explainability [150]. Figure 14: Challenging elements of interpretability and explainability. The Necessity of Interpretability and Explainability  
The demand for interpretability and explainability in AI systems is driven by the need for trust, accountability, and ethical considerations [152]. Trust is essential for the adoption and successful integration of AI systems, as users need to understand and believe in the decisions made by these systems [153]. Accountability ensures that AI systems comply with legal and ethical standards and can be audited when necessary [154]. Ethical considerations call for AI systems to adhere to the principles of fairness, transparency, and nondiscrimination [155]. Current Techniques for Interpretability and Explainability  
Various techniques have been proposed to enhance the interpretability and explainability of AI systems, ranging from inherently interpretable models to post hoc explanations for black-box models [156]. Some of these techniques include the following:  
Inherently interpretable models, such as decision trees, linear regression, and rule-based systems, are designed to be easily understood by humans, providing a direct relationship between input features and the model's output [157]. Local Explanations: Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) are methods that explain individual predictions by approximating a complex model's behavior using simpler, interpretable models for a specific instance [158; 159; 160]. Visualization Techniques: Techniques such as t-distributed Stochastic Neighbor Embedding (t-SNE) and activation can provide visual representations of high-dimensional data processed by AI models, enabling human users to understand the relationships and patterns present in the data [161; 162]. Remaining Challenges and Future Directions  
Despite progress in developing techniques for interpretability and explainability, several challenges still need to be addressed [150]:  
Trade-Off between Performance and Interpretability: Highly interpretable models often come at the cost of reduced predictive performance. Future research should focus on developing models that balance interpretability and performance [157]. Evaluation Metrics: The development of standardized evaluation metrics to assess the interpretability and explainability of AI models remains a challenge. Establishing universally accepted metrics will enable researchers to compare different techniques more effectively and drive further innovation [162]. Domain-Specific Solutions: Certain application domains require specific interpretability and explainability techniques. For example, in the medical field, explanations must be tailored to the knowledge and understanding of both clinicians and patients [163]. Further research is required to develop domain-specific solutions that satisfy unique requirements.