Explainable AI (XAI) methods can influence model performance in varying ways depending on the approach and implementation. Some XAI techniques have minimal or no direct impact on performance, while others may introduce trade-offs between interpretability and accuracy. For example, post-hoc explanation methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) analyze a model after training without altering its architecture or weights. These methods don’t inherently degrade performance because they operate separately from the model’s core functionality. However, they require additional computation time to generate explanations, which could affect real-time applications if explanations are needed on-the-fly.
In contrast, XAI techniques that modify the model itself—such as using inherently interpretable architectures like decision trees or linear models—often sacrifice predictive power for transparency. For instance, a deep neural network might achieve higher accuracy on complex tasks like image recognition compared to a decision tree, but the latter is easier to interpret. Similarly, techniques like attention mechanisms in transformers can enhance interpretability by highlighting relevant input features, but they might add computational overhead during training or inference. In some cases, constraints imposed by XAI (e.g., enforcing sparsity in feature selection) can even improve generalization by reducing overfitting, though this depends on the problem and data.
The impact of XAI also depends on how developers integrate it into the workflow. For example, using XAI to debug or refine a model (e.g., identifying biased features) might indirectly improve performance by addressing flaws in training data or architecture. However, if XAI methods are applied haphazardly—like adding excessive regularization to force simplicity—they might degrade accuracy. A practical example is using layer-wise relevance propagation (LRP) in neural networks: while it clarifies which input features influence predictions, implementing LRP requires careful tuning to avoid misinterpreting relevance scores, which could lead to misguided model adjustments. Ultimately, the relationship between XAI and performance is context-dependent, requiring developers to balance transparency goals with technical constraints.