What useful human review looks like in an AI-assisted hiring pipeline

Define the reviewer’s responsibility

Specify what the hiring team is deciding and what evidence it needs. Decision Agent automates the work of creating and running assessments, then produces evidence for review. This can reduce the time spent organising and conducting early-stage interviews without claiming that a report has established future job performance.

Automation can also handle configured screening steps. Recruiters remain responsible for the requirements, any agreed validation target, the review process and candidate exceptions. A useful human role is specific enough to explain what the reviewer can examine and change.

Make the evidence available

A reviewer needs the candidate’s response, the relevant behaviour and the reasoning behind the finding. In Conversations, the report links to transcript passages and playable clips. In Scenarios, findings refer to the responses in the work situation.

Distinguish an AI interpretation from its score calculation. The AI considers whether a behaviour was fully or partially demonstrated. A displayed score aggregates findings using fixed rules and adjustable weights. The calculation does not turn the interpretation into a validated prediction.

Allow challenge and correction

Recruiters can correct findings in the UI, affecting the report being reviewed. A useful correction has a reason grounded in the response and the agreed behaviour, rather than simply replacing one unexplained overall impression with another.

A sufficient body of corrections and outcome data may support future improvements. Reassessing retained responses against later performance outcomes is future work once that data exists; it should not be described as an automatic learning or retrospective-analysis feature available today.

Worked example: a surprising result

Illustrative example: a report suggests that a candidate did not clarify a request. The reviewer inspects the exchange and finds a relevant question the summary overlooked. They can challenge the finding using that source evidence.

If the exchange remains ambiguous, the next step could be a focused interview question. The important action is examining what the candidate demonstrated in context and deciding how much weight to give it.

Keep candidate support and assessment exceptions clear

Candidates can email Decision Agent for help with technical issues. The recruiter decides any exception to the assessment process. Support does not change the scenario for an individual candidate; the scenario is intended to simulate the work.

Make the contact route clear in the invitation and explain the campaign’s deadline. The team should be able to distinguish a technical problem from an assessment finding before deciding what happens next.

Review the process over time

Look for repeated disagreements, unclear behaviours and tasks that do not give candidates a useful opportunity to respond. Those patterns can help identify where the assessment design or instructions need attention.

For questionnaire automation, human-labelled answers and a recruiter-defined validation target provide a separate question-level process. The team currently offers to enable that automation after the target is met. This practical setup is distinct from proving that a complete hiring process predicts good employees.

Explore question-level automation, report interpretation and the interactive examples.