What are the types of Explainable AI methods?

Explainable AI (XAI) methods are techniques designed to make AI model decisions understandable to humans. These methods fall into three broad categories: intrinsically interpretable models, post-hoc explanation techniques, and visualization-based approaches. Each type addresses different aspects of transparency, catering to scenarios where developers need to debug models, meet regulatory requirements, or build trust with users.

Intrinsically interpretable models are designed to be transparent by nature. These include simple algorithms like linear regression, decision trees, or rule-based systems. For example, a decision tree’s structure—with its branching paths of “if-else” conditions—directly shows how input features lead to predictions. Similarly, linear regression coefficients indicate the weight of each feature in the final output. While these models are easy to explain, they often trade off complexity for interpretability, making them less suitable for highly nonlinear tasks like image recognition. Tools like scikit-learn’s DecisionTreeClassifier or statsmodels’ regression modules are commonly used here, as their logic is inherently visible.

Post-hoc explanation techniques are applied after a model has been trained, even for complex “black-box” models like neural networks. Methods like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) generate approximations of model behavior. For instance, LIME creates a simplified surrogate model (e.g., a linear regression) around a specific prediction to highlight influential features locally. SHAP borrows from game theory to fairly distribute the contribution of each feature to the prediction. These techniques work across model types but may sacrifice global accuracy for local interpretability. Libraries like shap and lime are popular implementations.

Visualization-based approaches use graphical tools to illustrate how models process data. Convolutional neural networks (CNNs), for example, can be analyzed using saliency maps, which highlight pixels in an image that most affected a prediction. Tools like TensorFlow’s What-If Tool let developers interactively probe model behavior by tweaking inputs and observing outputs. In NLP, attention maps show which words a transformer model focused on during text classification. While these methods are intuitive, they often require domain-specific customization. Frameworks like Captum (for PyTorch) and built-in TensorFlow utilities simplify creating such visualizations for debugging or stakeholder communication.