Human-in-the-Loop AI
A review checkpoint that keeps a person in control of consequential AI actions — the pattern that makes AI deployable in high-stakes work.
Business scenario
The AI is good, but not good enough to act unsupervised where mistakes are costly. Human-in-the-loop keeps a person as the decision-maker while the AI does the heavy lifting — draft, recommend, prioritize — turning "we can't trust it to act" into "we can ship it today."
Request & data flow
- The AI produces an output with a confidence signal.
- Routing decides: auto-handle the easy, high-confidence cases; send the rest to a human.
- A review UI presents the AI's suggestion, its evidence, and an accept/edit/reject control.
- The decision is logged, and edits feed a feedback loop for improvement.
Component-by-component
- AI model. Generates the suggestion and a confidence estimate.
- Confidence routing. Sends only the uncertain or high-stakes cases to people.
- Review UI. Makes the AI's reasoning and evidence reviewable, not just its answer.
- Audit log + feedback. Records who decided what, and turns corrections into training signal.
Why each service was chosen
Confidence routing is what keeps humans focused on the cases that need them instead of rubber-stamping everything. The review UI shows evidence, because a reviewer can only add value if they can check the AI's reasoning.
Alternatives considered
- Full automation. Appropriate only when the cost of a wrong action is low.
- Full manual review. Safe but doesn't scale; wastes the AI's leverage on easy cases.
Scaling considerations
As the model improves, raise the confidence threshold for auto-handling to move work off people — but keep sampling auto-handled cases to catch drift.
Security considerations
Log decisions immutably; control who can approve what; ensure reviewers see only data they're entitled to.
Failure handling
Fail toward human review, never toward silent automation. If confidence can't be computed, route to a person.
Observability
Track auto-handle rate, reviewer override rate, and downstream error rate. A rising override rate means the model is drifting or the threshold is wrong.
Cost considerations
Human review is the dominant cost — the design's whole economic goal is to safely shrink the share of cases that need it over time.
When not to use this
If actions are fully reversible and low-stakes, the review step is friction. Reserve it for consequential decisions.
Interview discussion points
- How do you decide which cases get human review?
- How do reviewer corrections improve the model?
- How do you keep the auto-handled path from drifting unnoticed?