Building Trustworthy AI: Avoid Model Drift and Unsafe Outputs

Summary: The video discusses three essential methods for ensuring AI models perform as intended after deployment. It highlights the importance of maintaining model accuracy and consistency to prevent undesirable outputs, such as immature language or inappropriate content.

Keypoints:

• AI models should maintain their intended output characteristics after deployment.
• Comparing model output to ground truth helps identify inaccuracies in predictions.
• Performance variance between development and production environments can indicate potential issues.
• Analyzing input data for substantial changes, such as demographic shifts, is crucial for maintaining model effectiveness.
• Implementing flags or filters can prevent the dissemination of harmful or sensitive information (e.g., PII, hate speech).