Neural networks struggle to explain their predictions directly because they are often considered "black-box" models. Their decision-making process involves complex layers of mathematical computations and interactions between neurons, making it difficult to trace how specific features contribute to predictions. This lack of transparency is a major concern, especially in critical applications like healthcare and finance.
To address this, techniques like Layer-wise Relevance Propagation (LRP), SHAP (SHapley Additive exPlanations), and LIME (Local Interpretable Model-agnostic Explanations) are used. These methods provide insight into which input features influence the model’s decisions most significantly. For instance, in an image classification task, visualization techniques like Grad-CAM highlight areas of the image that the model focuses on when making a prediction.
Despite these tools, the explanations are approximations rather than exact mechanisms. Developers should use neural networks cautiously in applications requiring accountability and interpretability, pairing them with these techniques or simpler models to ensure trust and transparency.