How AI Hiring Systems Analyze Video, Voice, and Resumes

Updated 9 November 2026 · Standard formula, shown below

AI hiring systems can help employers sort applications by extracting information from resumes and analyzing recorded interview responses. They may produce summaries, scores, or recommendations, but the methods and reliability vary by product; use the steps below to understand the process and check its results rather than treating a score as a hiring decision.

What You’ll Need

A job posting or list of role requirements to use as a comparison point.
The resume and video or audio interview materials you are authorized to review.
Access to the hiring system’s instructions, scoring criteria, and any available explanation of its outputs.
A way to record questions, evidence, and concerns so that assessments can be reviewed consistently.

Step-by-Step

Define the job-related criteria first. Identify required qualifications, relevant experience, skills, and evidence that would demonstrate each one. Separate essential requirements from preferences. Use the same criteria for every candidate, and avoid vague traits such as “culture fit” unless they are translated into observable, job-related behaviors. This makes it easier to judge whether an automated result is relevant.
Check what the system actually analyzes. Review the product’s documentation or settings before uploading or evaluating material. Resume tools may extract text, identify skills, or compare phrases with job criteria. Video tools may transcribe speech, assess answer content, or analyze audio or visual features. A system may use only some of these functions. Do not assume it reads facial expressions, emotion, or personality unless the documentation says so—and treat such claims cautiously.
Prepare consistent, accessible inputs. Use readable resume files and clear recordings where possible. Confirm that the system supports the file type and language. Give candidates clear instructions about recording, response length, and how to request an alternative format or accommodation. Poor audio, unfamiliar accents, background noise, camera quality, disability-related speech differences, or a nonstandard resume layout can affect automated processing.
Review resume extraction before relying on a score. Check whether the system correctly captured names, dates, qualifications, job titles, and skills. Resume parsers can misread columns, tables, headers, abbreviations, or scanned images. Compare extracted details with the source document, especially when a missing credential or date could change an assessment. A keyword match is not proof of skill, and different wording may describe equivalent experience.
Separate video transcription from interpretation. If a transcript is available, compare it with the recording when accuracy matters. Automated speech recognition can omit words, mishear technical terms, and handle accents or noisy recordings unevenly. Evaluate the substance of the answer against the same question and criteria for everyone. Do not treat speaking speed, accent, eye contact, facial movement, or camera presence as evidence of competence unless a specific behavior is genuinely necessary for the job and assessed fairly.
Understand any score or recommendation. Find out what inputs and criteria contribute to the result, what the score means, and whether it ranks candidates against one another or compares them with a threshold. Ask whether the system supplies evidence or only a number. A score is not a probability of success unless the provider defines and validates it that way. Avoid comparing scores generated under different questions, settings, or evaluation criteria.
Verify findings with human review. Have a qualified reviewer check relevant source material and note evidence for each criterion. Investigate apparent mismatches, such as a low score alongside clearly documented qualifications, or a high score with little supporting evidence. Do not let an automated ranking silently exclude a candidate. Use the system as decision support, with a clear process for correction and reconsideration.
Document the process and protect the data. Record the criteria, material reviewed, corrections made, and reasons for the outcome. Limit access to candidate information, use approved storage and retention settings, and follow applicable organizational policies and local requirements. Tell candidates what data is collected and how it is used as required. If an output may have been affected by a technical issue or an inaccessible assessment, pause and arrange a fair review.

Common Mistakes

Treating a score as objective truth: Scores depend on a model, its data, settings, and the inputs it receives. They can be wrong or reflect irrelevant patterns.
Confusing keywords with qualifications: A candidate may have relevant ability without using the system’s preferred terms, while a keyword may appear without meaningful experience.
Assuming every tool analyzes the same signals: Some products process text only; others use audio, video, or combinations. Verify features instead of guessing.
Using inconsistent questions or conditions: Different prompts, recording instructions, or scoring standards make comparisons less meaningful.
Overlooking accessibility and recording quality: Transcription or presentation issues can be mistaken for weak answers. Check whether the assessment gives candidates a fair way to participate.
Keeping data without a clear reason: Unnecessary copies of resumes, recordings, and transcripts create avoidable privacy and access risks.

Tips

Use a short, structured rubric with specific evidence examples for each criterion.
Review a sample of outputs manually before using a system at scale, and recheck it when questions, settings, or job requirements change.
Keep assessment questions and evaluation standards consistent across candidates for the same role.
Ask vendors what data features are used, how outputs are validated, what limitations are known, and how errors can be corrected.
Provide a contact route for candidates who need an accommodation, spot an inaccurate transcript, or want to raise a concern.
Use only information relevant to the role, and make final decisions through accountable human review.

Troubleshooting

The resume parser missed a qualification: Check the original file and extracted text. Correct the record if the system permits, or have a reviewer assess the source resume directly. If the file is scanned or unusually formatted, use an accessible text version where available rather than assuming the qualification is absent.

The transcript is inaccurate: Replay the relevant segment, check audio quality, and correct the transcript if the workflow allows. If the answer cannot be understood reliably, request an appropriate alternative or repeat assessment under consistent conditions; do not penalize the candidate for a processing failure.

The result conflicts with the evidence: Pause the decision and identify which criterion or input drove the result. Check for a wrong job profile, mismatched question, missing data, or a misunderstood score scale. Ask a second reviewer to assess the relevant evidence independently.

Candidates report unequal or inaccessible treatment: Stop relying on the affected output while the concern is investigated. Review instructions, input quality, and available alternatives, and involve the appropriate hiring, accessibility, or privacy contact. Keep a record of the resolution and apply any correction consistently.

You cannot explain a recommendation: Do not present it as a definitive finding. Seek documentation from the system provider or use a transparent, job-related review method whose evidence can be explained to the hiring team.

Frequently Asked Questions

Do all AI hiring systems analyze facial expressions?
No. Capabilities vary. Some systems process resumes or transcripts only; others may analyze audio or video features. Check the product documentation and settings.

Can a system decide who gets hired?
Some tools rank or filter applicants, but their outputs should be checked against job-related evidence and reviewed by accountable people. A score alone does not establish a candidate’s ability.

Does a high resume match score mean someone is qualified?
Not necessarily. It may reflect wording or extracted details. Verify experience and qualifications in the source material and assess them against the role’s actual requirements.

How can candidates correct a transcription error?
They can contact the employer or assessment administrator, point to the affected answer, and ask for a correction or another accessible way to provide the response.

Can accents or recording conditions affect results?
Yes. Speech recognition and other automated processing can be affected by accent, noise, microphone quality, and speech differences. Review uncertain outputs rather than treating them as reliable evidence.

What should employers ask a vendor?
Ask what data is analyzed, how criteria and scores are defined, what limitations and validation information are available, how candidates can request alternatives, and how errors and data retention are handled.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.