Amazon Augmented AI (Amazon A2I) is designed to enable human review workflows for machine learning predictions where human judgment is required. AWS documentation states that A2I allows organizations to integrate human reviewers into AI workflows to review low-confidence predictions or samples selected by business rules. This directly aligns with the requirement to involve employees in validating and improving translation outputs.

In this scenario, the translation model produces a confidence score , which is a common trigger used by Amazon A2I to route predictions to human reviewers. AWS explicitly describes that A2I supports use cases such as natural language processing, translation, content moderation, and document processing , where automated models may need human oversight to ensure accuracy and quality.

Amazon A2I provides managed workflows, reviewer task interfaces, and audit trails that allow employees to review, correct, and validate model outputs. The feedback collected through A2I can then be used to improve future model training , increasing translation quality over time.

The other options do not meet the requirement. Amazon SageMaker Clarify focuses on bias detection and explainability, not human review workflows. Amazon SageMaker Model Monitor is used to detect data drift and model performance degradation in production, not to involve humans in validating predictions. Amazon Bedrock Agents are designed to orchestrate tasks and interactions with foundation models, not to manage human-in-the-loop review processes.

AWS positions Amazon A2I as a core service for implementing human-in-the-loop machine learning , making it the correct solution for incorporating employees into a structured translation review process.