Interpretability

Interpretability is the extent to which a human can understand the cause of a model’s decision or the reasoning behind a result. It is often discussed alongside predictive accuracy and transparency, with the aim of making complex systems more understandable without compromising safety or accountability. Interpretability and explainability are related but not identical: interpretability refers to the overall intelligibility of the model, while explainability focuses on providing understandable justifications for specific outputs.

There are two broad approaches. Intrinsic interpretability uses models that are inherently easy to understand, such as linear models with limited features, decision trees, or rule-based systems. Post hoc interpretability produces explanations after a model has been trained, using methods that are model-agnostic or model-specific, such as feature attribution techniques (for example SHAP or LIME), partial dependence plots, or counterfactual explanations. Explanations can be faithful reflections of the model or approximations that aim to be more intuitive.

Evaluation of interpretability involves fidelity and understandability. Fidelity measures how accurately an explanation reflects the model’s behavior, while understandability assesses whether a target user can grasp the reasoning. Challenges include the potential for misleading or oversimplified explanations, user-dependent needs, and the trade-off between interpretability and accuracy, especially in high-stakes domains such as healthcare, finance, and criminal justice.

In practice, interpretability supports model debugging, accountability, and user trust. It remains an active area of research, with ongoing discussion about its definitions, metrics, and appropriate use across different applications and regulatory contexts.