As of 13 August 2026, AI can only partly compare candidates' interview performance.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
1 hourto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ita colleague
What the alternative costsA human hiring panel or HR specialist is the alternative; no price is stated in the available sources.
If this goes wrong: a candidate is scored unfairly or inconsistently, and the employer may face a complaint, a damaged hiring process or an employment dispute.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open the job description and the agreed interview scorecard, then copy only the objective essential criteria and competency definitions into a working document.
- Gather the same questions, interviewer notes and authorised transcripts for every candidate, and remove irrelevant personal data and protected-characteristic information before sharing anything with an AI tool.
- Check your organisation's recording, monitoring and recruitment privacy process, including whether candidates were informed and whether the material can be used for this comparison.
- Paste the prepared criteria, rubric, questions and labelled candidate evidence into the prompt, then ask the AI to produce the evidence table and bias checks.
- Compare every claimed piece of evidence in the output with the relevant transcript or note, correcting scores where the quotation is inaccurate, incomplete or taken out of context.
- Give the comparison and the source evidence to the interview panel, ask each member to record an independent decision against the job criteria, and retain the reasons and any adjustments under your normal recruitment process.
Prompt
Compare these candidates' interview performance for the same role using only the job criteria and interview evidence supplied below. Do not infer personality, protected characteristics, health, disability, age, ethnicity, religion, sex, pregnancy, sexual orientation, gender reassignment or other personal characteristics. Do not treat accent, fluency, confidence, eye contact, facial expression or communication style as evidence of ability unless it is directly and objectively relevant to a stated job requirement. Do not make the hiring decision. First, create a table with one row per candidate and one column for each competency. For every score, quote or closely paraphrase the specific evidence from that candidate's transcript or notes, and mark the evidence as missing where there is none. Use this scale: 0 = no relevant evidence, 1 = weak or unclear evidence, 2 = adequate evidence, 3 = strong evidence. Do not score a competency where the interview evidence does not address it. Then identify any material differences in the questions asked, probing, time allowed or evidence available between candidates. Flag possible inconsistencies, assumptions, missing evidence and wording that could create bias. State which parts require review by the interview panel. End with a neutral evidence summary for each candidate, not a recommendation or ranking. Job description and essential criteria: [PASTE JOB DESCRIPTION AND CRITERIA] Agreed competencies and scoring definitions: [PASTE RUBRIC] Interview questions asked to every candidate: [PASTE QUESTIONS] Candidate interview evidence, labelled by candidate and question: [PASTE REDACTED TRANSCRIPTS OR NOTES] Before using this comparison, confirm that every candidate was informed about recording or transcription where required, that the material is authorised for this use, and that irrelevant personal data has been removed.
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot decide whether an answer shows genuine competence when the evidence is ambiguous or the role depends on context that the transcript does not capture.
- AI cannot make an employment decision or accept responsibility for discrimination, inconsistent assessment or a challenge from a candidate.
- AI cannot repair unequal interviews, such as one candidate receiving more probing or more time, without the panel deciding how the evidence should be treated.
- AI cannot establish that recording, transcription and reuse of interview data are lawful and authorised for your organisation.
What caps this at PARTLY: legal accountability, judgement under ambiguity and consent and privacy.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 0 |
| Effort delta | 2 |
| Total | 6 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can AI rank interview candidates?
- Partly. AI can organise evidence and apply a supplied scoring rubric, but the hiring panel must check the evidence, address inconsistent interviews and own the decision.
- Can AI score interview answers?
- Yes, as a first pass against clearly defined, job-related criteria. It can miss context and reproduce bias, so compare each score with the source transcript and do not use it as the sole basis for selection.
- Is it legal to use AI to compare candidates in the UK?
- It depends on how the system is used, what data it processes and the safeguards around the decision, so this is not professional advice. A serious case needs an employment solicitor or suitably qualified HR and data protection specialist.
- Can AI decide who gets the job?
- No. AI can prepare an evidence comparison, but the employer and hiring panel remain accountable for a fair, explainable and lawful decision. Keep the final judgement with people who can review the evidence and challenge the model's assumptions.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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