The ability to describe how a system reached a decision.

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Describing why a system arrived at a decision is about explainability. Explainability is the ability to provide a human‑understandable account of the reasoning and steps that led to the outcome, including which features mattered and how they were weighed. This is crucial in risk contexts because it lets auditors, regulators, and users see the justification for a decision, assess potential biases, and understand the model’s behavior in real scenarios. Interpretability deals with how easily you can understand the model itself—its structure and internal logic—whereas explainability focuses on the external description of the decision process, which can be provided even for complex models. Transparency is about how open the system’s data, processes, and governance are; it supports explainability but isn’t the same as giving the decision’s reasoning itself.