or HITL, is when people are built directly into an AI system's process instead of just checking its output at the very end. A model runs, but a person reviews, corrects, or signs off before anything gets treated as final.

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‍Why it matters

No model gets everything right on its own, and some mistakes carry real consequences, a wrong medical read, a missed policy violation, a self-driving car misjudging a stop sign. Putting a person in that loop catches cases a model hasn't seen before and gives someone a name to point to when a decision needs explaining. That's part of why HITL shows up so often in industries with legal or safety stakes, where a machine deciding entirely on its own still isn't good enough.

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‍How teams build it

in Most teams that take this seriously design the review points into the pipeline from day one instead of adding them later once something's gone wrong. It's usually cheaper to catch an error at the review stage than to deal with the fallout after a bad decision ships.

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‍Where you'll see it

It looks different depending on where it's applied. In data labeling, a model often makes a first pass and a person fixes what it got wrong, which is faster than starting from scratch but still catches errors before they end up in training data. In content moderation, a flagged post usually goes to a human for the final call rather than getting removed automatically. Autonomous vehicle testing relies on it too, engineers review edge cases the car's system flags as uncertain before deciding how the model should handle similar situations next time.

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