How to review AI interview results: audit the reasoning

Here's how to review AI interview results: read one criterion at a time, starting with the criteria this role can't live without and the lowest scores. For each score, check that the quoted transcript excerpt shows the behavior the rubric describes. A score you can't trace to a quote isn't evidence yet, and your job is auditing that reasoning, not re-grading the candidate from scratch.

Key Takeaways

A score you can't trace to a quote isn't evidence. The hiring manager's job is to audit the AI's reasoning, not to re-interview the candidate in their head.
Read by criterion, not by total. Start with the criteria that matter most for this role, then the lowest and highest scores.
Run three tests on every score that matters: does the excerpt support it, does the reasoning match the rubric wording, and would another answer in the transcript change it.
Override when the evidence contradicts the reasoning or the rubric missed something the role needs. Don't override because the candidate "felt" stronger.
Write a one-line reason for every override. That note is how the rubric gets better and how your decision stays defensible.

What you're holding when the AI interview report lands

The guides that rank for this question tend to stop at a list: look at the score, the summary and the recording. That's a list of things to open, not a way to read them. You're one person with one candidate's report and one decision to make, so it helps to know exactly what's in front of you.

On Expert Hire, every AI interview report card carries the rubric criteria, a score per criterion, the transcript excerpts behind each score, any code the candidate wrote, and the AI's written reasoning for each score. The full transcript sits behind all of it. If you want the mechanics of how those scores get produced, we cover that in how AI interviews are scored. This piece is the reading side.

Who decides and where the AI's autonomy stops is a separate argument, and we've made it in our piece on the hiring copilot model. Here we're assuming the decision is yours and asking a narrower question: how do you check the work before you sign it?

Read by criterion, not by total

The total score is the least useful number on the page. It averages away the thing you care about, which is whether this candidate clears the bar on the two or three criteria this role can't survive without. A backend hire who scores well overall but weakly on data modeling is a different hire from one with the reverse profile.

So open the report in this order:

The must-have criteria for this role. Decide which ones they are before you look at any numbers, so the scores don't pick them for you.
The lowest score on the page. Low scores end candidacies, so they deserve the most scrutiny.
The highest score on the page. High scores get waved through, which is exactly why they need a second look.
Everything else, only if the first three reads leave you undecided.

This order also keeps you honest. If you read the total first, you'll skim the criteria looking for reasons to agree with it.

The one check that matters: does the quote support the score?

Every score on a structured report makes a claim: the candidate showed behavior X at level Y. The excerpt is the proof. Your core job is to read the quote next to the reasoning and ask whether they match. Three tests do most of the work.

Test 1: Does the excerpt show the behavior?

Read the rubric wording for the criterion, then read the quote. If the rubric says "identifies failure modes and proposes a mitigation", the excerpt should contain a failure mode and a mitigation. A confident answer that restates the question, or describes the concept in general terms, shouldn't score like an answer with a concrete example from the candidate's own work.

Test 2: Does the reasoning use the rubric's language?

Good reasoning points at the rubric. It says which part of the criterion the answer met and which part it missed. Weak reasoning drifts into adjectives like "strong understanding" or "communicated clearly" without saying what, in the transcript, earned them. When the reasoning could be pasted onto any candidate's report, it isn't reasoning about this one.

Test 3: Would another answer change it?

Candidates sometimes show a skill somewhere other than the question meant to test it. Someone who fumbled the direct question on testing might have written thorough test cases in the coding section. Before you accept a low score, search the full transcript and the candidate's code for the behavior the criterion describes. If it's there and the score ignored it, you've found a legitimate reason to act.

On coding criteria, the code itself is the excerpt. If the reasoning praises edge-case handling, the snippet should handle edge cases. That check takes a minute, and it's a better use of that minute than rereading the whole transcript.

Scores that should make you suspicious

After a few reports you'll start to see patterns. These are the ones worth stopping for:

High score, no example. The answer paraphrases the question back with confidence but never names a system, a decision, or a result.
Reasoning with no quote attached. A score that describes the candidate without citing anything they said or wrote.
A quote from the wrong criterion. An excerpt about deployment used to justify a system design score, or a communication quote propping up a technical one.
Every criterion landing on the same number. Real candidates are uneven. A flat profile can mean the questions didn't separate the criteria, which is a rubric problem.
A low score on something barely probed. One short question, a clarifying reply, then the interview moved on. That's an absence of evidence, which isn't the same as evidence of absence.
Code that contradicts the reasoning. The reasoning says "handles edge cases" and the snippet doesn't.

None of these automatically mean the score is wrong. They mean you haven't finished checking it yet.

Reviewers also tend to discount evidence they didn't gather themselves. In Jabarian and Henkel's field experiment on automated job interviews, covering 70,884 applications for entry-level customer-service jobs in the Philippines, human recruiters scored every interview from its transcript and recording and made every hiring decision. When the AI had run the interview, their offers leaned less on their own interview scores and more on a standardized language test.

That's one setting, a high-volume customer-service funnel, and it says nothing direct about engineering hiring. The authors read it as recruiters trusting an independent test over an interview they didn't conduct. Our takeaway is narrow: quietly discounting a whole interview is a blunt response to doubt, and checking the specific quotes behind the scores that matter is the sharper one.

When to override AI interview scores, and when not to

Overriding is part of the job, not a failure of the tool. The EU AI Act classes AI used to evaluate candidates as high-risk, and its Article 14 on human oversight requires such systems to be built so the people overseeing them can correctly interpret the output, stay aware of automation bias, and disregard, override or reverse it. The question is what counts as a valid reason.

Override when:

The evidence contradicts the reasoning. The quote doesn't show what the reasoning claims, or the code doesn't do what the reasoning praises.
The transcript holds evidence the score missed. Test 3 turned up the behavior somewhere else in the interview.
The rubric missed something the role needs. The candidate was scored fairly against a criterion that turns out to be the wrong one for this job. Here, override the decision and fix the rubric too.

Don't override when:

The candidate "felt" stronger or weaker than the score, and you can't point to a line in the transcript that says so.
You're rescuing a total. Bumping two criteria so a candidate you already like clears the bar is the unstructured interview sneaking back in.
The reason is pedigree. A previous employer or a school name isn't evidence for a criterion the interview measured.

There's a research reason to hold this line. Sackett, Zhang, Berry and Lievens (2022, Journal of Applied Psychology) re-estimated selection validities and put structured interviews at roughly .42, down from the .51 Schmidt and Hunter reported in 1998. Structured interviews still ranked first. Gut-feel overrides quietly turn a structured process back into an unstructured one.

Write the override down in one line

An override you don't write down can't be checked, can't be defended, and can't improve anything. It also can't be told apart from the gut-feel kind. One line is enough, as long as it points at evidence:

[Criterion]: changed from [original] to [new] because [quote, or line of code] shows [the behavior the rubric describes]. Rubric change needed: [yes or no, and what].

Two things happen when you do this consistently. First, your decision survives a question from the candidate, a colleague or legal, because it rests on the same transcript the AI used. Second, patterns show up. If the same criterion keeps getting overridden for the same reason, the rubric wording is the problem, not the candidate.

That's where the fix belongs. On Expert Hire, hiring managers can edit the rubric criteria before interviews run, and calibration mode lets you run a known candidate through the AI and tune the rubric until the score matches your expectation. Your override notes tell you what to tune. Our methodology page explains how the rubrics are designed in the first place.

If you're passing the decision to a final round, attach the notes. The engineer running a human-led final interview should know which criteria you doubted, so they can probe exactly those instead of starting cold. And if you owe the candidate feedback, the same evidence makes it concrete; see our interview feedback examples for how to phrase it.

Frequently asked questions

How long should a hiring manager review AI interview reports for?

Long enough to run the three tests on the criteria that decide this hire, and no longer. You don't need to read the transcript start to finish. Read the must-have criteria, the lowest and highest scores, and search the transcript only when a score fails a test. If every score you checked holds up, you're done.

Should I review AI interview scores if the total already clears the bar?

Yes, and the high scores especially. A total can clear the bar on the strength of criteria that don't matter for this role while hiding a weak score on one that does. High scores built on vague or rehearsed answers are also the easiest thing to miss in a quick review.

What should I do if a score has no quote or reasoning behind it?

Treat it as unscored, not as a pass or a fail. Go to the transcript and look for the behavior yourself, then note what you found. If your tool routinely produces scores without evidence, that's a signal about the tool. Our structured interview software guide covers what to demand from one.

Doesn't overriding the AI undo the point of a structured interview?

Only if the override is undocumented. A written override that cites the transcript is still structured: same rubric, same evidence, a better reading of it. The unstructured version is changing a score because of a hunch. For how the two formats compare on consistency, see AI interview vs human interview.

Is there evidence that people make better decisions with the AI interview report than without it?

Some, with caveats. In the "Better Together" experiments by Aka and colleagues, recruiters who could see an AI interview report shortlisted candidates who passed the final human interview at rates 17.5 to 20 percentage points higher than resume-only shortlisting. Two of the four authors list an affiliation with micro1, whose AI interviewer was studied, and 75% of invited candidates never completed the interview, so read it as a promising vendor-linked signal, not settled proof.

How to review AI interview results: audit, don't re-grade

Reviewing AI interview results well comes down to one habit. For every score that matters, find the quote, check it against the rubric wording, and look for evidence elsewhere before you accept a low number. Override when the evidence says so, write the reason in one line, and let those notes fix the rubric.

A score you can't trace to a quote isn't evidence. Your job is to audit the reasoning, not re-grade the candidate.

If you want to see what that looks like in practice, the AI interview platform page describes the report card you'd be reviewing: a score per criterion, with the transcript and the AI's reasoning behind each one.

By TK, Growth at Expert Hire. Last updated September 29, 2026. Reviewed by Anand Suresh, CPO at Expert Hire.