Deliver user training that explains the system's limits: Applying deployment policies and best practices,
User training on the system's limits and how to review outputs directly counters uncritical reliance by staff.
The question
After deploying an AI assistant, a company finds that frontline staff accept its outputs uncritically, including occasional errors, because they do not understand its limits. Applying deployment policies and best practices, which control most directly mitigates this misuse risk? The company wants the best-fit deployment control.
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- Re-run the provider's pre-release conformity assessment to re-confirm the model met its original launch requirements.Plausible but wrong: re-confirming release conformity does not change how staff interpret and use the outputs.
- Increase the model's training compute in a future version so as to raise its overall accuracy on difficult edge cases.Almost right in targeting errors, but more compute is a development change and does not address user over-reliance now.
- Expand the vendor's indemnification clause so the company is covered when errors reach customers.Plausible but wrong: indemnity shifts financial risk but does not prevent staff from misusing the outputs.
- Deliver user training that explains the system's limits and how to critically review its outputs before acting. ✓Correct because IV.C.1 lists user training among deployment policies and best practices, directly addressing uncritical reliance.
The trap
Applying a development or contractual remedy to what is fundamentally a user-behavior risk. How to remember it
User training on the system's limits and how to review outputs directly counters uncritical reliance by staff.
How many of these would you get right?
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Part of the Certsqill AIGP question bank · Understanding How to Govern AI Deployment and Use ·
Every answer, right and wrong, comes with its own explanation.
