What is your approach to auditing AI models post-deployment?

After deploying an AI model, I follow a structured audit process that includes multiple key steps. First, I establish a baseline for performance metrics such as accuracy, precision, recall, and F1 score based on the model's training data. Next, I conduct regular evaluations on hold-out test sets and real-world data to monitor drift in performance over time. It’s also essential to implement monitoring tools that can flag discrepancies or drops in performance. I ensure that any potential biases are evaluated through fairness metrics tailored to the specific application. Lastly, I document all findings and adjustments for compliance and transparency purposes, and I have a feedback loop for continual improvement.