Interpretability vs Explainability — A Distinction That Matters When AI Makes Consequential Decisions

We covered Explainable AI — XAI — earlier in this series. But there is a distinction within that field that is worth understanding clearly: the difference between interpretability and explainability. These terms are often used as synonyms. They are not.

Interpretability

Interpretability refers to the degree to which a human can directly understand the mechanism by which a model produces its output. An interpretable model is one whose internal workings can be directly read, traced, and understood — not through an external explanation, but by looking at the model itself.

A decision tree is interpretable. You can trace the exact path from input to output: if age is greater than 50 and income is below X, classify as high risk. Every decision point is visible. You do not need to explain it — you can simply read it.

A large neural network with billions of parameters is not interpretable in this sense. No human can read a neural network and understand why it produced a specific output by examining the parameters directly.

Explainability

Explainability refers to the ability to produce a human-understandable description of why a model produced a particular output — even when the model itself is not directly interpretable. Explanations are generated after the fact, using separate tools or techniques applied to the model’s behaviour.

SHAP values, which we covered in the XAI post, are an example. SHAP does not make a neural network interpretable — it does not let you read the network. But it generates an explanation of which input features contributed most to a specific prediction. The explanation is a simplified approximation of the model’s behaviour, not a direct view of its mechanism.

The key distinction

Interpretability: you can understand the model by looking at it. The mechanism is transparent by design. Explainability: the model is too complex to directly understand, but you can generate a post-hoc explanation of its behaviour for a specific output. The explanation is a reconstruction, not a direct view.

An important implication: post-hoc explanations can be wrong. Because they are approximations of a model’s behaviour rather than direct views of its mechanism, they can fail to capture the true reason for a decision — particularly for complex models in unusual input regions. A convincing-sounding explanation does not guarantee that it accurately reflects how the model actually reached its conclusion.

Why this matters for high-stakes AI

In regulated industries — banking, healthcare, maritime safety — the difference matters significantly. A regulator who asks ‘why did this system make this decision?’ may be satisfied by an explainability tool in some contexts but require a genuinely interpretable model in others. For the highest-stakes decisions, an explanation that is an approximation may not be sufficient. The question is not just ‘can you explain this?’ but ‘can you actually see inside this?’

The simple rule

Interpretable: you can read the model. Explainable: you can generate an account of its behaviour. Interpretability is built in by design. Explainability is added after the fact. For the highest-stakes decisions, interpretability is always preferable where achievable.

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