AI Can Review the Submission. Who Reviews the AI?
One of the awkward things about testing AI review is that a wrong answer can come with a very convincing explanation.
The response is clear. It refers to details in the submission. It sounds as though the instruction was understood.
Then you look at the image yourself and notice that it has misread the discount code or made an assumption about something that is not visible.
That gap between a plausible answer and a dependable decision has been a big part of our work on AI-assisted activity reviews at BrandChamp.
The appeal is easy to understand
Admins review submissions from ambassadors to check whether they have completed an activity correctly.
Depending on the activity, that can involve reading text, looking at images, or checking video against a set of instructions.
Some of that work is repetitive. If AI can handle the clear cases, admins could spend more time on submissions that need judgment or a conversation with the ambassador.
That is a useful goal.
But a review has consequences. An incorrect approval can let an unsuitable submission through. An incorrect rejection creates extra work and a poor experience for someone who may have completed the activity properly.
So the question is whether we can rely on the decision enough to let it affect the workflow.
Start by checking the activity itself
Not every activity is suitable for AI review.
Some have requirements that are visible in the submission. Others depend on context that is difficult to verify.
An image might clearly show a product. It may not establish who created the image, when it was taken, or whether it belongs to the person submitting it.
Similarly, a written response may mention all the expected ideas without proving that a real-world action happened.
We need to be clear about what the available evidence can support.
Otherwise, we are asking the model to fill in the gaps and then treating its assumptions as a review result.
Instructions reveal their own weaknesses
Testing also exposes ambiguity in the activity instructions.
A human admin may know what “show the product clearly” means for their program. The model needs more detail. Does the label need to be readable? Does the entire product need to appear? Are different packaging versions acceptable?
Some of that context should be supplied to the reviewer. Some belongs in clearer instructions for the ambassador as well.
This has been a useful side effect of the work. When a review is difficult to judge consistently, the problem is sometimes in the task definition.
Adding “do not make assumptions” helps establish the intended behaviour, but we still have to test whether the system follows it.
A score needs evidence behind it
A review score looks precise. It is tempting to set an approval threshold and assume everything above it is safe to automate.
But the score only becomes useful when we compare it with actual decisions.
We need to examine where the model and a human reviewer disagree. Was the requirement unclear? Did it miss a visible detail? Did it assume ownership of content? Was the human decision itself inconsistent?
Those differences tell us more than a single overall match rate.
They also help us decide which cases can be automated and which should remain in manual review.
Manual review needs to be easy
For uncertain submissions, the admin should be able to see what the AI considered and make the final decision.
The explanation needs to be specific enough to inspect. If a required detail is missing, say which one. If something cannot be verified, make that uncertainty visible.
We also need to learn from overrides. When an admin changes an AI decision, that is useful evidence about the review system.
I want to understand whether AI is reducing the total work, including checking and correcting its decisions. Processing more submissions is only useful if the resulting decisions hold up.
The goal is to give admins a reviewer they can use with appropriate confidence. Getting there requires paying close attention to the cases where it gets things wrong.