Human approval exists not because we distrust AI, but to check a decision’s impact at the right place. Low-risk classification and sending a price to a customer do not need the same approval level.
Risk and reversibility are considered together
When a model’s output is wrong, can it be taken back? Does a wrong answer only produce a draft, or does it send a message to the customer? These questions show where the approval point must be.
An example in three levels
- Automatic: labelling, summarising or internal search results.
- Review: a quote or content draft going to the customer.
- Double approval: money movement, data deletion or permission changes.
An approval screen should not be only “accept”
The human should see which source was used, where the model is unsure, and what the action’s result will be. That way approval becomes a meaningful control, not a thoughtless barrier.
Source / further readingNIST — AI QuickStart for security analysisThis article does not copy the original content of the source; it is WYN’s practical interpretation. For current headlines, check the publication date and the source page separately.