How to review an AI-assisted shortlist without surrendering the decision

Putting a recruiter after an AI score does not automatically create meaningful oversight. A reviewer who sees only a ranking, has little time, and cannot access the underlying document may end up approving the tool's output by default.

Design the review around what the person needs to check and what they are allowed to change.

Start with the job rubric and source evidence

Show reviewers the agreed criteria and the resume passages supporting the result where available. They should be able to distinguish candidate-provided information from a generated summary or inference.

Ask them to investigate claims the document does not support. A polished summary can still attach the wrong date, exaggerate a responsibility, or treat a team accomplishment as individual ownership.

Give reviewers a way to record disagreement without forcing the original output to disappear. The team may need to understand later why a recommendation changed.

Inspect who was left out

Reviewing only the top-ranked applications cannot reveal every useful candidate the system missed. Include a controlled sample from outside that group and look for repeated patterns in overlooked evidence.

Choose the sample and review depth according to the role, the consequences of error, and the advice of the people responsible for governance. A fixed sample size borrowed from another employer is not a universal safeguard.

Keep technical failures and incomplete analyses visible as exceptions. They should have a route to manual review rather than automatically joining the bottom of a ranking.

Give the reviewer real authority

Specify whether the reviewer can progress an application, request clarification, correct extracted data, or pause use of the tool. Name the person who resolves questions about the criteria themselves.

Provide enough time and training for the task. If performance expectations reward processing volume alone, a formal review step may become a rapid series of approvals.

Learn from disagreements

Record why the human and tool differed: unreadable text, different interpretation, missing context, or an unsuitable criterion. Use those categories to decide whether the remedy belongs in parsing, assessment, or the hiring process.

Check whether a later product update changes behavior on previously reviewed examples. Keep candidate-facing explanations accurate about how the tool is used.

Responsibility remains with the organization making the hiring decision. A useful review process makes it possible for a person to examine evidence, challenge a recommendation, and act on what they find.

Further reading

• Recruitment rewired: an update on the ICO’s work on the fair and responsible use of automation in recruitment | ICO
• Here’s what jobseekers need to know about automated recruitment decisions | ICO

Related guides

• Test screening quality
• Write feedback the team can evaluate
• AI resume screening: how it works and where it can go wrong