What techniques can be used to improve the interpretability of deep learning models?
Instruction: Discuss methods to make the decision-making process of deep learning models more understandable to humans.
Updated
Example Answer
I would begin with the question the explanation needs to answer: why this prediction changed, which inputs matter locally, or whether the model uses a spurious shortcut. Those are different goals.
For a neural network, I might use integrated gradients, SHAP, a local surrogate such as LIME, or controlled input ablations. I would explain the method's assumptions and test whether the explanation is stable and faithful enough for its intended use. An attention heatmap is a useful diagnostic, but attention weights alone do not prove why the prediction occurred. Feature attribution also does not establish a causal relationship or demonstrate that a model is fair.
For an image classifier, I would compare the highlighted region with perturbation tests and a parameter-randomization sanity check. If the map looks convincing even after randomizing learned weights, I would question the explanation method. Where a simpler model performs comparably, using it can be more useful than adding a complex explanation layer.
Follow-up
Masking image regions can create unrealistic inputs, so compare several perturbation choices and check whether the conclusion changes.