Purpose
In regulated hiring, a decision has to be defensible: a person owned it, the reasoning is recorded, and the evidence is reviewable. Exterview is built so the AI sharpens that decision without ever making it. This page covers the standard, how to read a report well, and how to decide whether a step should block progression or just inform it.The standard
A person decides
No adverse decision is made autonomously. The AI proposes and evidences; a person decides.
Reasoning is recorded
The why behind a decision is captured, not just the outcome.
Evidence is reviewable
Every score traces to session evidence and the configuration version that
produced it.
Content, not biometrics
Scoring is grounded in what a candidate says, never facial analysis or tone.
How to read a report well
An Exterview report is decision support, not a verdict. Reviewing it well means reading the evidence behind the score, not just the number.1
Start with the evidence, not the score
Read what the candidate actually said and how it maps to the rubric before
you look at the number. The score summarizes the evidence; it does not
replace it.
2
Check the citations
Every claim traces to a moment in the session. Spot-check the citations that
most affect the decision.
3
Look for a critic flag
If the critic layer flagged the output, read the flag first once that
capability is available. See AI Observability.
4
Make the call
You own the decision. Use the report to make it faster and more defensible,
not to outsource it.
Good habits
Read across candidates
Compare on the same rubric dimensions rather than on overall impression.
Weight gated dimensions
Give the most attention to the dimensions your configuration treats as gates.
Record the why
Note the reason for the decision. It feeds auditability and the post-hire
loop.
Assign clear reviewers
Every gate has a named human owner. Configure this in Governance.
Gate vs. signal: what should block progression
Every evaluation step in Exterview is configured as a gate or a signal. Getting this split right is what keeps your pipeline both fair and efficient. Over-gate and you reject good candidates on one weak dimension; under-gate and nothing is actually filtered. See Operations for where this is set.1
Ask if it is truly disqualifying
If a weak result on this dimension alone should end the process, it is a
gate. If not, it is a signal.
2
Default to signal
When unsure, make it a signal. Signals inform a human decision; gates remove
it. Reserve gates for real must-haves.
3
Limit the number of gates
A few well-chosen gates filter effectively. Many gates compound into a
funnel that rejects strong candidates on a single off dimension.
4
Keep gates human-reviewed
A gate controls progression, but the decision at the gate is still owned by
a person, not an autonomous reject.
Common mistakes
Over-gating
Turning every dimension into a gate rejects good candidates on one weak area.
Gating soft skills rigidly
Nuanced dimensions usually belong as signals a human weighs, not hard gates.
No gates at all
If nothing gates, the pipeline filters nothing and reviewers drown.
Silent auto-reject
A gate should route to human review, never auto-reject without a person.
Related
Responsible AI
The platform-wide human-review standard.
Auditability
The traceable record behind every decision.
Evaluation Design
Mapping competencies to the right steps.
Scoring Quality
Keeping scores consistent across reviewers.