Human-in-the-loop AI

Human-in-the-loop AI is a design approach in which a person reviews, approves, or corrects AI outputs at defined points before or after the system acts. Deterministic steps run automatically; judgment-based or high-impact decisions, such as an offer, a promotion recommendation, or a policy exception, are routed to a person for validation before anything is committed.

In practice this means the system does the assembly and the human makes the call where a call is needed. The human's decisions are also the training signal: overrides, reversals, and flagged outputs feed back into prompt tuning, retrieval indexes, and retraining, and they are the evidence on which a system earns wider autonomy. Darwinbox routes outputs for hiring, offers, goal-setting, and performance to human reviewers by design.