## 4 Recent Advances and Future Directions
### Model Interpretability Techniques  
The challenge of interpreting complex ML models has driven the development of various techniques aimed at enhancing their transparency and trustworthiness. Widely recognized methods such as LIME and SHAP provide post hoc explanations by approximating the contribution of input features to model predictions. These techniques are essential in domains where understanding a model's decision-making process is critical for regulatory compliance, ethical considerations, and user trust [298; 299; 300]. Complementing these approaches, IGs address the attribution problem by integrating the gradients of a model's output with respect to its input features along a path from a baseline input to the actual input. This method ensures attribution scores are faithful to the model while maintaining completeness and sensitivity. IGs are particularly effective for DNNs, providing detailed insights into feature contributions in tasks such as image recognition and NLP. For example, in medical imaging, an IGs can highlight specific regions in an image that influence diagnostic predictions, enabling practitioners to better understand and validate the model's outputs [301; 302; 303]. In addition to feature attribution, anchors provide a rule-based approach to local interpretability by generating \"if-then\" rules that describe the key conditions under which model predictions remain consistent. Unlike general attribution methods, anchors offer highly precise and localized explanations that are both interpretable and generalizable across similar input data. These rules are particularly valuable in high-stakes applicationssuch as fraud detection or medical diagnostics, where decision stability and clarity are paramount [304; 305]. Beyond understanding feature contributions, counterfactual explanations focus on identifying the minimal changes to input data required to alter a model's output. This method provides actionable insights, making it intuitive and user-friendly for non-technical stakeholders. For example, in loan applications, a counterfactual explanation might suggest that increasing an applicant's income by a specific amount would lead to loan approval. By offering clear, actionable feedback, counterfactual explanations empower users to make informed decisions based on model outcomes and encourage model accountability [306; 307]. Another promising area of research is the development of inherently interpretable models, which achieve transparency by design rather than relying on post hoc explanations. Models like GAMs with pairwise interactions offer a clear and intuitive understanding of how input features contribute to predictions. These models decompose complex relationships into additive components, allowing stakeholders to visualize and interpret the influence of individual features and promoting trust in model predictions [308; 309]. Attention mechanisms in DL, particularly in architectures like transformers, have further advanced interpretability by revealing how models prioritize specific parts of the input data. Attention maps provide visual insights into what a model \"focuses\" on during decision making. For instance, in NLP, attention maps can highlight the words or phrases most influential to a prediction, such as identifying keywords in sentiment analysis. Similarly, in computer vision, attention mechanisms pinpoint regions of interest in an image, such as abnormalities in medical scans. In applications like healthcare, these insights are invaluable for validating AI-driven recommendations, while in autonomous systems, they clarify decision-making processes, improving reliability and safety [310; 311; 312]. Together, these advanced interpretability techniques--IGs, anchors, counterfactual explanations, inherently interpretable models, and attention mechanisms--extend the capabilities of traditional methods like LIME and SHAP.