Surrogate models in Explainable AI (XAI) act as simplified approximations of complex, opaque models (like neural networks) to make their decisions understandable. They work by mimicking the behavior of the original model using interpretable structures—such as decision trees, linear models, or rule-based systems—while preserving key patterns in the predictions. This allows developers to analyze how inputs affect outputs without needing to decipher the inner workings of the original “black-box” model. For example, a complex deep learning model for medical diagnosis might be approximated by a surrogate decision tree that highlights the most critical patient features (e.g., age, blood pressure) influencing predictions.
Surrogate models are often built using techniques like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations). LIME, for instance, generates local explanations by perturbing input data points and observing how the original model’s predictions change. A surrogate linear model is then trained on this perturbed data to approximate the behavior around a specific prediction. Similarly, SHAP uses game theory to assign feature importance values globally or for individual predictions. For example, in a credit scoring system, SHAP could reveal that income and debt-to-income ratio are the top factors driving a model’s rejection of a loan application. These methods let developers test hypotheses, debug models, or comply with regulatory requirements by providing human-readable explanations.
However, surrogate models have limitations. They are approximations, so they may not perfectly capture the original model’s logic, especially for highly nonlinear or context-dependent decisions. For instance, a surrogate decision tree might oversimplify a deep learning model’s reliance on subtle pixel patterns in image classification. Developers must validate surrogate accuracy by comparing its predictions to the original model’s outputs and use surrogates as one tool among many in XAI. Despite these trade-offs, they remain practical for enabling transparency in regulated domains like healthcare or finance, where stakeholders need to trust and verify automated decisions. Tools like scikit-learn for building surrogate models or libraries like Lime and SHAP are commonly used to implement these techniques.