The correct responsible AI dimension is Explainability . AWS defines explainability in its Responsible AI framework as “Having mechanisms to understand system behavior.” The AWS Generative AI Lens similarly characterizes explainability as “Understanding and evaluating system outputs.”
The wording of the question directly matches this AWS definition. The organization wants to understand and evaluate outputs produced by its generative AI system. Explainability concerns the mechanisms, methods, and information that allow developers, operators, auditors, customers, and other stakeholders to understand why an AI system produces particular behavior or outputs.
Explainability can involve examining influential features, reasoning about model behavior, analyzing response patterns, providing evidence or supporting information, and using interpretability techniques appropriate to the model architecture. For generative AI systems, the goal is not necessarily to expose every internal mathematical operation of a foundation model. Instead, organizations establish mechanisms that make system behavior sufficiently understandable for the intended use case and associated risks.
Controllability is different. AWS defines controllability as having mechanisms to monitor and steer AI system behavior. Examples include configuring guardrails, adjusting system behavior, establishing user controls, or restricting what an AI application can do.
Safety focuses on blocking or reducing harmful outputs and preventing misuse. It addresses risks where AI behavior might cause harm to users or other stakeholders.
Fairness concerns the different effects that an AI system can have on groups of stakeholders and seeks to detect or mitigate unwanted disparities or bias.
AWS recommends identifying responsible AI risks and selecting appropriate evaluation metrics for the relevant dimensions throughout development and deployment. Explainability is specifically measured when teams need to assess whether stakeholders can understand system behavior.
Because the requirement explicitly centers on understanding and evaluating AI-generated outputs , A. Explainability is the exact AWS-aligned answer.
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