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AI does the legwork and a person approves before the work is acted on.
The AI reads, extracts, drafts or recommends; a person approves, edits or rejects, and only approved work moves forward.
Human in the loop (HITL) describes any AI system where a person checks the model's output at a defined point before it is acted on. The AI reads, extracts, drafts or recommends. A person approves, edits or rejects. Only approved work moves forward.
Two related terms: human on the loop means the system acts on its own while a person watches and can intervene; human out of the loop means fully automatic. Many systems mix all three, depending on the risk of each step.
A draft, extracted fields or a suggested reply, with a confidence score where available.
A draft invoice, extracted form fields, a suggested reply, a flagged image, each with a confidence score where the model provides one.
Confident, low-risk items pass through; doubtful, high-value or unusual ones go to review.
High-confidence, low-risk items may pass straight through. Low confidence, high value or unusual items go to a review queue.
The reviewer sees the source beside the proposal and fixes fields quickly.
The reviewer sees the source (the scanned bill, the original message) next to the proposal and can fix fields quickly.
Who approved what, when and what they changed: your audit trail.
Who approved what, when, and what they changed. This is your audit trail.
Edits become test cases and training data, so errors fall over time.
Edits become test cases and training data, so the error rate falls over time.
In Fint, the AI agent proposes records and nothing is recorded until a person confirms.
In our own Fint product, the AI agent proposes records and a person confirms them in an Inbox; money is never recorded without that confirmation.
AI drafts replies and a person sends them, at least until quality is proven.
AI drafts replies; an agent sends them, at least until quality is proven.
Medical, credit and hiring decisions; the EU AI Act requires oversight for high-risk systems.
Medical, credit and hiring decisions. The EU AI Act requires human oversight for high-risk AI systems.
Approving hundreds of items unseen is theatre. Show the source and sample-check reviewers.
If reviewers approve hundreds of items a day without looking, the human step is theatre. Show the source, highlight uncertain fields, and sample-check reviewers.
Measure accuracy per field and relax review only where the data shows it is safe.
Measure accuracy per field and per document type, and relax review only where the data shows it is safe.
Every edit is free labelled data. Keep it for evaluation and retraining.
Every edit is free labelled data. Store it for model evaluation and retraining.
No. The AI still does most of the reading and typing; the person spends seconds checking instead of minutes entering. And review can be reduced to exceptions once accuracy is measured.
Combine risk and confidence. Route anything above a money threshold, anything the model is unsure about, and a random sample of the rest. Adjust the rules as you collect accuracy data.
For some uses, yes. The EU AI Act requires human oversight of high-risk systems, and UK GDPR gives people rights around solely automated decisions with significant effects. Check the rules for your sector and market.
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