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Book a demo (opens in a new tab)For hiring teams reading their first flagged report: what integrity signals record, what they cannot tell you, and how to act on a flag without treating it as a verdict.
An integrity signal in an AI interview is a record of events that might mean a candidate had help: leaving the window, pasting text, another voice, or a submission matching someone else's. It is evidence for review, not a verdict. Good systems separate harmless events from real flags and never let a machine reverse a decision on its own.
Remote interviews made it easier to look things up, read from a second screen, or have someone else answer. As AI tools that write answers in real time spread, hiring teams asked interview software to notice. Most AI interview products now record some form of integrity or proctoring signal. The hard part is not collecting events. It is deciding what they mean.
Most systems log events during the session, such as:
Each event is a fact about the session. None of them, alone, is a fact about the candidate’s honesty. People switch tabs to find a file the interviewer asked for. Connections drop. Two people can write similar solutions to a small task.
A useful system decides which events can never count against a candidate. In EmployLabs, proctoring events from the candidate’s browser are appended to a log that is never edited, and the severity of each event is set on the server from the kind of event, never from what the candidate’s browser reports. A liveness heartbeat and a client disconnect are never treated as violations on their own, and the proctoring tab a recruiter sees leaves both out.
That matters because the alternative is a report full of red marks that mean nothing, and reviewers who learn to ignore them.
So the right question is not “what is the integrity score?” It is “what exactly happened, and does it change what I believe about this answer?”
An integrity signal should point a person at the right minute of the interview, not replace the person.
Integrity flags are where automated hiring can do the most harm, because a false flag can end a good candidate’s process with nobody noticing. Two design choices limit that harm.
Flags reach a person.In EmployLabs, when take-home submissions are found to match, the earlier peer’s verdict is withheld and handed back to a person: if that candidate had already passed, their application moves back to the stage before the verdict and a review card is raised for a recruiter. If they had already moved past that point, nothing moves automatically; a note goes on their timeline and an operator is emailed. An offer is never taken back by a machine.
Integrity can be part of the rubric, explicitly. If honesty or compliance is a real requirement of the job, put it in the rubric. In EmployLabs, a rubric dimension named for compliance or integrity is always must-pass, so it is visible and deliberate rather than a hidden modifier.
The best defence against both cheating and false accusations is evidence a person can check. In Naira’s reports, every piece of evidence is tied to a numbered candidate turn and stores the candidate’s exact words. A quote the candidate never said is removed by code, and a rating left with no supported quote is withdrawn. If the grader cannot grade at all, the report says “Not graded — review the transcript” and the decision goes to a person.
When a flag and a rating disagree, that traceability lets a reviewer look at the exact words instead of arguing about a number. Reading an AI interview report covers the order to read the rest of it in.
A candidate for a backend role switches tabs twice during the coding module and pastes a short block into the editor. The report shows both events with timestamps.
A reviewer who reads only the label might reject. A reviewer who follows the steps above opens the transcript at those moments. The first tab switch came right after Naira asked the candidate to look at a config file they had mentioned; the paste was a log line the candidate had copied from that file. Their explanation in the transcript is specific and matches what they typed. The second switch is unexplained, so the reviewer adds one question about it to the hiring manager round.
Nobody was rejected on a timestamp, and the one real doubt got a real answer. That is the job an integrity signal should do: point a person at the right minute of the interview.
Live voice interviews and take-homes raise different risks. In a live call, the main questions are about outside help in the moment, and the strongest check is a follow-up on the same topic, which an adaptive interviewer can ask on the spot. In a take-home, the risk is a shared or copied submission, which only shows up when submissions are compared. EmployLabs handles the two differently: proctoring events are logged during a live session, and a match between take-home submissions withholds the earlier peer’s verdict for a person to review. For how the live interview itself is run, start with what an AI interviewer is.
Every finalist arrives with an interview report that points back to what they actually said.
