Explainability Engineering

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Explainability engineering takes the broad idea of “explainable AI” and turns it into practical tools and workflows that help people understand how a model makes decisions. The goal is to show why a prediction was made, what factors influenced it, and whether that behavior meets expectations or requirements. This can mean choosing models that are naturally easier to interpret or adding methods that highlight important features or provide example-based explanations.

Beyond the explanations themselves, this work also includes creating policies and documentation that define what should be shown and how explanation quality is measured. These processes often connect to audits, safety checks, and regulatory standards, since many AI decisions must be reviewable by humans.

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