Human in the loop is the design choice to keep a person in the AI’s decision path rather than letting it act fully on its own. The person might approve an action before it executes, review outputs the model flags as uncertain, or correct the cases it gets wrong so they become training and evaluation data. It’s how teams ship useful automation without betting everything on the model being right. It is one practice within human-centered AI, which designs systems around the people who use them.
The skill is placing the human where it counts. Route everything to a person and you’ve lost the automation; route nothing and you’ve shipped unreviewed autonomy. The usual answer is to gate by stakes and confidence — auto-handle the routine, high-confidence cases and escalate the risky or uncertain ones. Over time, as evaluation shows the model is reliable on a slice of work, you widen what runs without review.
See AI vocabulary in plain English for this term alongside the other language of models, data, agents, and infrastructure.
Hear it from the guest
“How much autonomy is too much autonomy, which all are the places where we need to have human in the loop. And there's no right answer to it.”
Quotes lightly edited to remove filler words.
Sources
- AI Act Explorer: Article 14, Human oversight — Requires high-risk AI systems to be designed so people can oversee them, including the ability to disregard, override or reverse the system's output; checked October 25, 2026.
- OWASP: LLM01:2025 Prompt Injection — Lists requiring human approval for high-risk actions among the controls that limit what a compromised model can do; checked October 25, 2026.
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