Your question is Reliability and Trustworthiness. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What strategies would you use to ensure the reliability and trustworthiness of AI model outputs?
Address this as a practical model evaluation task. Explain how you would define quality, select metrics, construct representative evaluation data, analyze errors, validate outputs before release, and monitor performance after deployment. Discuss how you would handle uncertainty, distribution shift, biased or incomplete data, non-deterministic outputs, and failures where aggregate metrics appear strong but important cases perform poorly.