A hospital pilots an AI model that recommends dosage adjustments for a high-risk intravenous medication. Before letting the system influence real patient care, the clinical team runs extensive testing against rare edge cases, requires that every recommendation pass through a clinician for review, and continuously monitors the model's error rate after go-live so problems are caught quickly. Which responsible AI principle are they primarily applying?

Reliability and safety is the principle that AI systems must perform consistently and safely under real-world conditions, including edge cases, and that teams build in monitoring and human checkpoints to catch problems before and after deployment. Testing against rare edge cases and tracking the model's error rate once it is live are both direct efforts to ensure the system behaves safely across the range of situations it will actually encounter, which is the core of this principle. The accountability principle is incorrect because accountability is about establishing who is responsible for a system's outcomes and putting governance structures in place to assign that responsibility; it describes organizational ownership rather than the technical testing and monitoring practices described here. The inclusiveness principle is incorrect because inclusiveness concerns designing systems that work well for people of varied abilities and backgrounds, which is not what this scenario addresses. The transparency principle is incorrect because transparency involves helping people understand how and why a system produces its outputs, such as through documentation or explanations, rather than the edge-case testing and error-rate monitoring described in the scenario.