Can neural networks explain their predictions?

Neural networks cannot inherently explain their predictions in a human-interpretable way. Their decision-making process is driven by complex mathematical transformations across layers of neurons, making it difficult to trace how specific inputs lead to outputs. For example, a convolutional neural network (CNN) classifying images might activate patterns in hidden layers that correspond to abstract features like edges or textures, but these internal representations are not easily mapped to logical reasoning. This lack of transparency is a key limitation, especially in high-stakes domains like healthcare or finance where understanding “why” matters as much as accuracy.

However, developers can use techniques to approximate explanations. Methods like LIME (Local Interpretable Model-agnostic Explanations) create simplified models to mimic a neural network’s behavior around specific predictions. For instance, if a model rejects a loan application, LIME might highlight income level and credit history as influential factors. Attention mechanisms in transformers (e.g., BERT) provide another layer of insight by showing which input tokens (words) the model “pays attention to” when making predictions. Tools like SHAP (SHapley Additive exPlanations) quantify feature importance by analyzing how predictions change when inputs are perturbed. These methods don’t reveal the model’s inner logic but offer post-hoc approximations.

Despite these tools, explanations remain incomplete. For example, SHAP values might indicate that pixel regions in an X-ray influenced a diagnosis, but not how the model interpreted those pixels. Additionally, different explanation methods can produce conflicting results, leaving developers to reconcile inconsistencies. Techniques like saliency maps in CNNs highlight input regions affecting outputs but often fail to distinguish between causal relationships and correlations. While progress continues in interpretability, neural networks still lack the intrinsic ability to articulate reasoning like rule-based systems. Developers must weigh the trade-offs between model complexity and explainability based on their use case.