Model comparison using Explainable AI (XAI) is the process of evaluating and contrasting machine learning models by analyzing how they make decisions. Unlike traditional metrics like accuracy or F1-score, XAI techniques focus on understanding the internal logic, feature importance, and decision boundaries of models. This approach helps developers identify strengths, weaknesses, and biases across models, even when their performance metrics are similar. For example, two models with identical accuracy might rely on different features or exhibit varying levels of robustness to noise, which XAI tools can expose.
A practical example involves comparing a decision tree and a neural network for a classification task. Using SHAP (SHapley Additive exPlanations), you might discover that the decision tree heavily weights a specific feature (e.g., “user age”) while the neural network spreads importance across multiple features (e.g., “location” and “browsing history”). Similarly, LIME (Local Interpretable Model-agnostic Explanations) could show that the neural network’s predictions for individual cases are more sensitive to minor input changes compared to the decision tree. These insights go beyond accuracy scores, revealing trade-offs in interpretability, stability, and real-world applicability.
For developers, XAI-driven comparison is valuable for debugging and model selection. Suppose you’re deploying a credit scoring model: XAI techniques can highlight whether a black-box model (e.g., a gradient-boosted tree) uses legally protected features like “gender” indirectly, even if accuracy is high. Comparing this to a simpler logistic regression model with explicit coefficients might reveal compliance risks. Tools like partial dependence plots or feature attribution maps allow side-by-side analysis of how models handle edge cases or data shifts. This process ensures technical teams choose models that balance performance, transparency, and regulatory requirements effectively.