Interpretability and explainability are both concepts focused on making machine learning models understandable, but they address different aspects of transparency. Interpretability refers to how directly a human can grasp why a model makes specific decisions based on its inherent structure. For example, a linear regression model is interpretable because its coefficients clearly show how each input feature contributes to the output. Similarly, decision trees are interpretable because their branching logic can be traced step-by-step. In contrast, explainability involves using external methods to generate understandable reasons for a model’s behavior, even when the model itself is complex or opaque. Explainability tools like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) create post-hoc explanations for models like neural networks, which are otherwise difficult to dissect.
A key distinction lies in the model’s design versus the techniques applied to it. Interpretability is a property of the model architecture. For instance, a logistic regression model’s weights directly indicate feature importance, making it interpretable by design. On the other hand, explainability is a set of processes applied after a model makes predictions. For example, using SHAP values to explain why a black-box model (like a gradient-boosted tree) predicted a high risk of loan default for a specific customer, even though the model’s internal logic isn’t inherently clear. Another example is using attention maps in image classification models to highlight which pixels influenced the prediction, even if the model’s layers are too complex to interpret manually.
For developers, the choice between interpretability and explainability depends on the problem context. If regulatory compliance (e.g., GDPR’s “right to explanation”) or debugging is critical, using inherently interpretable models like decision trees or linear models simplifies auditing. However, if accuracy demands require complex models like deep neural networks, explainability tools become essential to bridge the gap between performance and transparency. Practically, this might mean training a interpretable model for a credit scoring system where regulators need clear rules, while employing explainability techniques for a medical diagnosis model where high accuracy is non-negotiable but clinicians still need to validate predictions. Both concepts aim for transparency but serve different stages of the model lifecycle—interpretability in design, explainability in analysis.