Most human-in-the-loop AI checks fail for one of three reasons: the reviewer lacks context, lacks authority, or stops paying attention once the AI is right often enough. That third failure, automation bias, is the one most governance conversations skip, and it turns a real safeguard into a rubber stamp.
At the CIO 100 Conference, Doug Shepherd, head of offensive security at Cloudflare, pointed out that many organizations think they have a human in the loop on their AI systems when what they actually have is a human adjacent to the loop. “Formative governance,” he called it.
His test is simple. For a human-in-the-loop check to work, the person has to have the knowledge and context to act without the AI, and the authority to override it. Miss either one and the loop falls apart.
I’ve been building and running my own agent processes lately, sitting in the reviewer seat myself, and I don’t think Shepherd’s framing goes far enough. Here’s what I’d add.
Is the human still paying attention?
Knowledge and authority get most of the attention in this conversation, but there’s a third failure mode that’s just as common: automation bias. Give a reviewer both context and override power, and the loop still degrades over time. When the AI is right 95 percent of the time, people stop scrutinizing the other 5 percent. The checkpoint is still there on paper. In practice, it’s turned into a rubber stamp.
This is why the loop has to be designed to keep the human engaged, not just technically empowered. Authority on an org chart doesn’t hold up against a hundred consecutive correct outputs.
The real danger isn’t a missed error
The obvious risk is that a bad output slips through unreviewed. The sharper risk is what a hollow human-in-the-loop process does to accountability. Once you can say a human reviewed something, you’ve created an audit trail. That trail lets you deploy more aggressively and shifts the liability onto a person who never had a real chance to catch the problem.
That’s worse than having no human in the loop at all. An honest “this system runs autonomously” prices the risk correctly. Performative governance hides the risk while making everyone involved feel covered, including the auditors and regulators who are supposed to be checking your work.
Design the checkpoint like you’d onboard an intern
The people reviewing AI decisions usually aren’t looking at code and don’t have the skills to. What they need is the same thing you’d hand an intern: an artifact that lays out the decision, the recommendation, and the rationale behind it, in a form they can actually evaluate.
That means the review layer isn’t something you bolt on after the system works. You have to build it in from the start. If a reviewer has to dig through pages of verbose output to find the one number that matters, they’ll approve by default, and you’re back to a rubber stamp. Consumable, decision-ready output is a design requirement, not a nice-to-have.
And like an intern, the human in the loop needs to actually push back. Approving isn’t the job. The job is to ask whether the recommendation lines up with what the business needs.
What this means for how you build
None of this means slowing down everything with more approval steps. A human in the loop can always choose not to respond, which breaks the loop by design, like a circuit breaker. You can patch around a circuit breaker, but it’s there for a reason. The goal isn’t to eliminate friction. It’s to put friction in the right place, sized to the actual risk, with a reviewer who’s still paying attention and looking at something they can actually judge.
If your AI transformation strategy treats this as a checkbox, a name in a sign-off field, you don’t have an effective human in the loop. Building it right takes the same discipline as any other part of the system: know what the reviewer needs to see, know what authority they actually have, and design for the fact that vigilance fades unless you engineer against it.
FAQ
What does human in the loop mean in AI systems?
It means a person reviews or approves an AI system’s output before it takes effect. For the check to be real, the reviewer needs both the context to judge the output and the authority to change or stop it.
Why do human-in-the-loop checks fail?
Three reasons: the reviewer doesn’t have enough context to evaluate the AI’s output, the reviewer lacks the authority to override it, or automation bias sets in and the reviewer stops scrutinizing outputs that are usually correct.
What is automation bias?
Automation bias is the tendency to trust an automated system’s output without checking it, especially once the system has been correct often enough that scrutiny starts to feel unnecessary. It’s a common way human-in-the-loop checks become rubber stamps.
