What is human-in-the-loop AI?
Definition
Human-in-the-loop AI is a system design in which a person reviews, corrects or approves an AI system's output before it has a consequential effect, so accountability for the decision stays with a human.
How it works
The AI system proposes: a score, a flag, a summary or a recommendation. A person reviews it with access to the underlying evidence, can override or disregard it, and makes the decision. The review point is built into the workflow rather than left to chance.
Why it matters
AI output can be wrong, and decisions about people carry real consequences for them. Keeping a person accountable at the decision point allows errors to be caught and candidates to be heard, and regulations increasingly expect meaningful human oversight of AI used in employment.
Example
An assessment integrity check flags that another person appeared on camera during a session. Instead of the candidate being rejected automatically, a recruiter reviews the flag and the candidate's explanation, and decides whether it matters.
AuraSync's approach
In AuraSync, AI output is decision support. A person at the hiring organization reviews results and integrity flags and makes the hiring decision, and any candidate can ask for a human to review an AI-generated score or recommendation.
Limitations
Human review only helps if it is real. When reviewers are rushed or defer to the recommendation by default (automation bias), the human step becomes a rubber stamp. Reviewers need time, the underlying evidence and the authority to disagree.