Different Explainable AI Methods
Categorizing Explainable AI Techniques
Explainable AI (XAI) methods can be categorized in various ways to better understand their capabilities and limitations. The four main ways to classify XAI techniques are:
Model Agnostic vs. Model Specific
Model agnostic methods, such as LIME, SHAP, and Partial Dependence Plots, can be used with any machine learning model. In contrast, model-specific methods, like attention mechanisms for neural networks or tree interpreters for decision trees, only work for certain model types.
Global vs. Local Explanations
Global explanations provide insights about the overall importance of features in a model, while local explanations focus on the factors influencing a specific prediction for an individual data point.
Data Type
XAI techniques can be tailored for different data formats, including text, images, tabular data, and graph-structured data.
Explanation Type
Explanations can be visual (e.g., correlation plots, feature importance), based on feature importance scores, derived from specific data instances, or provided by simpler surrogate models.
Exploring Explainable AI Techniques
LIME (Local Interpretable Model-Agnostic Explanations)
LIME is a model-agnostic, local explanation method. It approximates the complex, global model with a simple, linear model in the vicinity of a specific data point. By perturbing the input features and observing the changes in the model's output, LIME can determine which features are most influential for a particular prediction.
SHAP (SHapley Additive exPlanations)
SHAP is another model-agnostic technique that uses Shapley values to quantify the contribution of each feature to the model's output. It does this by randomly masking out features and observing the changes in the prediction, similar to the permutation feature importance method.
Partial Dependence Plots (PDPs)
PDPs visualize the average marginal effect of a feature on the model's output. They show the relationship between a feature and the predicted outcome, allowing you to understand how changes in a feature value impact the model's predictions.
Tree Interpreter
The Tree Interpreter method is designed for decision tree-based models, such as decision trees and random forests. It can provide local and global explanations by tracing the decision-making process through the tree structure.
CNN Visualizations
For image classification models, techniques like saliency maps, activation maps, and gradient-based methods can highlight the regions of an image that are most influential in the model's predictions. This helps identify whether the model is focusing on the right visual cues.
Permutation Feature Importance
Permutation feature importance measures the increase in a model's prediction error after randomly shuffling the values of a feature. This quantifies the feature's importance by the drop in the model's performance when that feature is removed from the decision-making process.
Counterfactual Explanations
Counterfactual explanations identify the minimal changes to the input features that would result in a different model prediction. This helps understand which features are most critical for a particular prediction outcome.
By understanding these diverse XAI techniques and how they can be categorized, you can better choose the right methods to interpret the inner workings of your machine learning models and build trust in their predictions.
To learn more about these XAI methods, I recommend checking out the following resources: