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Purpose

How Exterview keeps AI-assisted evaluation fair, explainable, and under human control. Our approach aligns with the responsible-AI principles Microsoft and others use: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. This page states what we do under each; it describes Exterview’s own practices and is not a claim of Microsoft certification.

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Our commitments

Human review

People review and approve outcomes. AI assists; it does not decide.

No autonomous adverse decisions

No fully autonomous decision rejects or disadvantages a candidate.

Explainability

Every score traces to the evidence and rubric behind it.

Content-based scoring

Evaluation is based on what a candidate says, not face, tone, or biometrics.

Disclosure of AI interaction

Candidates are told when they are interacting with an AI system, and consent is captured.

Fairness by design

Rubrics are role-relevant and applied consistently, to reduce bias.

Reliability and safety

We are building an automated system to check AI outputs for quality and safety before and after they’re used, before a person acts on them.

Privacy and security

Your data is kept separate from other customers’ and is never used to train shared models.

Accessibility and inclusion

Evaluation surfaces are designed with accessibility in mind, and candidate accommodations are available on request.

Accountability

Named human owners are accountable for decisions, with a full audit trail.
Exterview does not make autonomous hiring decisions. Reports are decision support; a human always owns the outcome.
If a customer turns on identity verification, it works by checking a photo ID against a face photo. That check is handled separately from scoring and is not used to make any hiring decision, it only confirms who the candidate is.

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How this maps to responsible-AI principles

Exterview’s practices line up with the six principles most enterprises, including Microsoft, use to evaluate responsible AI.

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Capabilities and limitations

Being transparent means being clear about the edges of the system, not only its strengths.

What it is

Decision support: structured evidence and a score to help a person decide faster and more consistently.

What it is not

A decision-maker. It does not autonomously hire, reject, or rank a candidate out of the process.

Where it needs care

Below the calibration volume of a new deployment, treat scores as directional and keep human review especially close. See Running a Fair Pilot.

What it does not infer

It does not score on face, voice tone, emotion, or other biometrics.

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FAQs

No. No fully autonomous adverse decision is made. A human reviews and decides.
Yes. Candidates are informed when a session is AI-conducted, and consent is captured before the session begins.
No. Scoring is content-based, grounded in what a candidate actually says or submits. Separately, if a customer turns on identity verification, that feature checks a photo ID against a face photo purely to confirm identity, it plays no part in scoring or any hiring decision.
Yes. Scores trace to the underlying evidence and the rubric version applied.
Through role-relevant rubrics, consistent application, human review, and traceability, not opaque trait inference.
A critic layer evaluates and scores each output. We’re building automated observability over every step; this is being rolled out progressively. See AI Observability.
Evaluation surfaces are designed with accessibility in mind, and candidate accommodations are available on request. Contact your Exterview representative to arrange them.

AI Observability

How outputs are scored, monitored, and kept reliable.

Auditability

How every score traces to its evidence and reviewer.

Security

Access, encryption, and keeping each customer’s data separate.

Compliance FAQs

Straight answers for security and procurement reviewers.