# Evaluate and release
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 The evaluate and release focus area provides best practices for making a responsible release decision, given the release criteria from [Release criteria](release-criteria.md), the evaluation datasets from [Dataset planning](dataset-planning.md), and the AI system design from [System planning](system-planning.md). The release decision is a binary (yes or no) decision that aggregates the results of testing the AI system against each individual release criterion. A well-designed decision factors in the compounded uncertainty in the individual criterion decisions, that is have we met each criterion with the required confidence (for example, are we at least 95% confident that the release criteria have been satisfied?). If a specific release criterion is not met, release may still be possible if the residual risk is disclosed through Monitoring transparency mechanisms. Otherwise, you should return to [System planning](system-planning.md) to review options for baking and filtering mitigations. 

**Topics**
+ [System evaluation](raier01.md)
+ [Aggregate results](raier02.md)
+ [Address unmet release criteria](raier03.md)