An AI audit team is reviewing an organization's process for decommissioning retired AI models. Which risk is MOST critical to address in the decommissioning process?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you need to confirm that downstream systems relying on the model's outputs have migrated to the replacement system before shutdown. If you don't, those systems can fail or fall back to degraded processes. Archiving the code is useful, but it's not the most critical decommissioning risk.
Full Explanation
The critical risk is confirming that downstream systems relying on the model's outputs have migrated to the replacement system before shutdown. Premature decommissioning can make dependent systems fail or fall back to degraded processes. Archiving the decommissioned model's code in source control helps preserve knowledge, but it is not the most critical decommissioning risk. Verifying that all training notebooks use the latest ML framework is development hygiene. Publishing performance metrics in an annual AI report is transparency, not a decommissioning risk control.