Human Judgment in AI: Implications for AI Governance
The most dangerous AI hallucinations aren’t the ones that sound wrong.
They’re the ones that sound like something an expert would say.
I encountered one during a Kubernetes security architecture discussion.
A custom hardened “golden image” had been built for Kubernetes worker nodes. In AWS, Amazon EKS supported using a custom Amazon Machine Image (AMI) for its worker nodes.
The question was whether Azure AKS could take a comparable approach: consume our own hardened image when deploying worker nodes.
A colleague brought an AI-generated implementation path into the discussion.
And it was convincing.
It provided detailed steps, referenced real Azure components, and used the right terminology. It sounded entirely plausible.
There was just one problem.
At the time, AKS did not support consuming a customer-built base OS image for its managed worker nodes in that way.
Microsoft’s documentation was explicit: Microsoft provided the base OS image for AKS agent nodes, and customers could not replace it through the underlying Azure infrastructure APIs.
I had already evaluated the AKS capability and reviewed the documentation, so I flagged the discrepancy.
The purpose of building the hardened image was to deploy the AKS nodes from it. If AKS could not consume it, the proposed implementation failed at its fundamental requirement.
The AI-generated steps depended on a capability the platform did not support.
And that is what stayed with me.
This was not an obviously bad AI answer.
Most of the technologies and concepts surrounding the incorrect conclusion were real.
You needed enough platform knowledge, and enough willingness to verify the answer, to recognize that something fundamental was wrong.
That is a different kind of AI risk.
AI can compress the time it takes to produce an answer. It cannot eliminate the expertise required to know whether that answer makes sense.
As AI systems become more technically fluent, fluency itself becomes a weaker signal of correctness.
An answer can use the right terminology, reference real technologies, and follow a logical sequence while still describing something the platform does not support.
That changes the value of expertise.
Knowing every answer from memory becomes less important.
Knowing what should be possible.
Knowing which assumption needs verification.
Knowing when similar platforms do not expose the same capabilities.
And knowing when to stop a convincing answer from becoming an architecture decision.
We often talk about keeping a “human in the loop.”
But human oversight is only meaningful when the reviewer has enough domain knowledge to challenge the output.
Otherwise, we risk turning human oversight into:
Human approval of a plausible answer.
As AI gets better at sounding right, how are we ensuring the humans reviewing its answers know when to question them?
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