What is Human in the Loop?

Human in the loop (HITL) is a way of running AI in which a person reviews, approves or corrects a model's output before it is used or stored, so that a human judgment sits between the model and the decision, label or action that follows.

Human in the Loop (HITL) in AI: Definition

Human in the loop shows up wherever a model proposes and a person disposes: a reviewer confirms or corrects suggested labels, an engineer reviews AI-generated code before it is merged, an operator approves an agent’s request before it runs. The person is there to catch what the model gets wrong. Volume can work against that: Anthropic reported in March 2026 that Claude Code users accept 93% of manual permission prompts, and warned that constant approvals lead to approval fatigue, where people stop paying close attention to what they approve.

The record that review leaves can claim more than it shows. CUBIG’s article on when a human approval becomes ground truth (November 2026) describes a Findings of ACL 2025 study in which GPT-4 scored a weighted F1 of 0.47 against labels made without its suggestions and 0.79 against labels made with them on screen. The study covered subjective labeling tasks done by crowd workers.

Common forms:

label verification: people confirm or adjust labels a model proposed
output review: a person checks a generated answer, draft or piece of code before it is used
action approval: an agent asks permission before running a tool or changing a system
exception routing: the model handles confident cases and sends uncertain ones to people

How is human in the loop different from human on the loop?

Human in the loop: a person reviews each output before it takes effect.
Human on the loop: the system acts on its own while a person monitors and can intervene.
In either setup, an approval or a lack of intervention records only that the person did not object. It does not show that they would have reached the same answer without the model, a gap described under automation bias.

Where does human in the loop break in AI work?

Human in the loop breaks when an approval is filed as something stronger. A suggested label a reviewer kept can end up in a column called verified, or in the evaluation set that grades the model that suggested it. The model is then graded partly on answers it helped write.

Keeping the review useful means keeping its record: whether a label was made by a person who saw no suggestion, by a person who saw one and kept or changed it, or by a model alone. That record is a form of data provenance at the level of a single label.

Frequently asked questions

What is an example of human in the loop?

A labeling workflow in which a model suggests a category for each support ticket and a reviewer confirms or corrects it before the label is stored.

Does human in the loop make AI output correct?

Not by itself. Review can catch errors, but people who see a suggestion tend to follow it, so an approval shows that a person did not object, not that the output was independently checked.

What should be recorded in a human in the loop workflow?

At minimum, whether each label or decision came from a person without a suggestion, a person who saw a model's suggestion and kept or changed it, or a model alone.