Review AI-assisted recruiting recommendations against the underlying job-relevant evidence before anyone relies on them. A fluent summary can omit context, invent a connection, or turn missing information into a negative conclusion.
Keep an accountable human decision maker in the process. This workflow does not establish legal compliance or authorize automated employment decisions.
Use the distinction in Refery's actual recommendations
In one Refery search, the candidate recommendation described relevant work and why the role interested the candidate, then named a question the hiring team should still test. Evidence of broad ownership did not become a claim that every specialist requirement was proven.
The earlier calibration profiles made a different uncertainty explicit: candidate interest was unconfirmed. A hiring team's positive reaction to a profile was not recorded as the candidate's consent.
Those are useful checks on an AI-assisted summary too. Does it preserve the open question? Does it distinguish an example profile from an interested candidate? Does it turn a recommendation into a supposed hiring decision?
The anonymized search story documents those distinctions. This lesson concerns the content of a recommendation; it does not claim that a particular AI system made or validated the decisions.
Define the permitted task
Be specific about whether the tool organizes records, summarizes supplied information, or suggests questions for review. Do not quietly expand an administrative helper into an unexplained candidate-selection system.
Use approved tools and appropriate data access. Do not infer sensitive characteristics or personal circumstances from application materials.
Check the recommendation
| Check | Reviewer action |
|---|---|
| Source | Locate the evidence supporting each material statement |
| Accuracy | Verify contribution, dates, and role context where relevant |
| Relevance | Connect the statement to an essential responsibility |
| Missing information | Preserve uncertainty instead of treating absence as failure |
| Consistency | Apply the same job criteria across candidates |
| Decision | Record the human reasoning and next step |
| Correction | Fix misleading summaries before they are reused |
NIST’s AI Risk Management Framework addresses trustworthiness in AI design, use, and evaluation. It provides broader risk-management context; it is not a hiring-specific approval or guarantee. NIST AI Risk Management Framework.
Test with synthetic cases
Before relying on a workflow, use fictional profiles that contain ambiguous scope, missing information, and different wording for comparable experience.
Check whether the tool preserves the distinctions and whether reviewers can detect errors. A small test does not establish general fairness or validity, but it can expose obvious failure modes.
Make disagreement useful
If a reviewer rejects a recommendation, record why. Look for recurring errors in the workflow and stop using outputs that cannot be checked reliably.
Do not force managers to accept a score simply because it appears precise.
Keep the candidate process accountable
Provide the appropriate route for correcting inaccurate information under the company’s process. Changes to tools, data, or decision use should receive the relevant review.
Use the common candidate evidence record, inbound review guide, and hiring manager training checklist. Discuss an evidence-based hiring process with Refery.